On-device Convolutional Neural Network Models for Assistant Systems

ABSTRACT

In one embodiment, a method includes receiving a user input comprising one or more words at a client system, wherein each word comprises one or more characters, inputting the words to a convolutional neural network (CNN) model stored on the client system, accessing a plurality of character-embeddings for a plurality of characters, respectively, from a data store of the client system, generating one or more word-embeddings for the one or more words, respectively, based on the accessed character-embeddings by processing the accessed character-embeddings with one or more convolutional layers and one or more gated linear units of the CNN model, determining one or more tasks corresponding to the user input for execution based on an analysis of the one or more word-embeddings by the CNN model, and providing an output responsive to the user input based on the execution of the one or more tasks at the client system.

PRIORITY

This application claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Patent Application No. 62/923,342, filed 18 Oct. 2019, whichis incorporated herein by reference.

TECHNICAL FIELD

This disclosure generally relates to databases and file managementwithin network environments, and in particular relates to hardware andsoftware for smart assistant systems.

BACKGROUND

An assistant system can provide information or services on behalf of auser based on a combination of user input, location awareness, and theability to access information from a variety of online sources (such asweather conditions, traffic congestion, news, stock prices, userschedules, retail prices, etc.). The user input may include text (e.g.,online chat), especially in an instant messaging application or otherapplications, voice, images, motion, or a combination of them. Theassistant system may perform concierge-type services (e.g., makingdinner reservations, purchasing event tickets, making travelarrangements) or provide information based on the user input. Theassistant system may also perform management or data-handling tasksbased on online information and events without user initiation orinteraction. Examples of those tasks that may be performed by anassistant system may include schedule management (e.g., sending an alertto a dinner date that a user is running late due to traffic conditions,update schedules for both parties, and change the restaurant reservationtime). The assistant system may be enabled by the combination ofcomputing devices, application programming interfaces (APIs), and theproliferation of applications on user devices.

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g. profile/newsfeed posts, photo-sharing, event organization, messaging, games, oradvertisements) to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the assistant system may assist a user toobtain information or services. The assistant system may enable the userto interact with it with multi-modal user input (such as voice, text,image, video, motion) in stateful and multi-turn conversations to getassistance. As an example and not by way of limitation, the assistantsystem may support both audio (verbal) input and nonverbal input, suchas vision, location, gesture, motion, or hybrid/multi-modal input. Theassistant system may create and store a user profile comprising bothpersonal and contextual information associated with the user. Inparticular embodiments, the assistant system may analyze the user inputusing natural-language understanding. The analysis may be based on theuser profile of the user for more personalized and context-awareunderstanding. The assistant system may resolve entities associated withthe user input based on the analysis. In particular embodiments, theassistant system may interact with different agents to obtaininformation or services that are associated with the resolved entities.The assistant system may generate a response for the user regarding theinformation or services by using natural-language generation. Throughthe interaction with the user, the assistant system may usedialog-management techniques to manage and advance the conversation flowwith the user. In particular embodiments, the assistant system mayfurther assist the user to effectively and efficiently digest theobtained information by summarizing the information. The assistantsystem may also assist the user to be more engaging with an onlinesocial network by providing tools that help the user interact with theonline social network (e.g., creating posts, comments, messages). Theassistant system may additionally assist the user to manage differenttasks such as keeping track of events. In particular embodiments, theassistant system may proactively execute, without a user input, tasksthat are relevant to user interests and preferences based on the userprofile, at a time relevant for the user. In particular embodiments, theassistant system may check privacy settings to ensure that accessing auser's profile or other user information and executing different tasksare permitted subject to the user's privacy settings.

In particular embodiments, the assistant system may assist user via ahybrid architecture built upon both client-side process and server-sideprocess. The client-side process and the server-side process may be twoparallel workflows for processing a user input and providing assistanceto the user. In particular embodiments, the client-side process may beperformed locally on a client system associated with a user. Bycontrast, the server-side process may be performed remotely on one ormore computing systems. In particular embodiments, an arbitrator on theclient system may coordinate receiving user input (e.g., audio signal),determining whether to use client-side process or server-side process orboth to respond to the user input, and analyzing the processing resultsfrom each process. The arbitrator may instruct agents on the client-sideor server-side to execute tasks associated with the user input based onthe aforementioned analyses. The execution results may be furtherrendered as output to the client system. By leveraging both client-sideand server-side processes, the assistant system can effectively assist auser with optimal usage of computing resources while at the same timeprotecting user privacy and enhancing security.

In particular embodiment, the assistant system may implement anatural-language understanding module associated with the client-sideprocess using a convolutional neural network (CNN) model. The CNN modelmay be compact and can be efficiently executed on a client system, e.g.,a smartphone or an AR/VR headset. In particular embodiments, the CNNmodel may be based on an architecture as disclosed herein. For thisarchitecture, the input may be each single word of an utterance by auser. A word-embedding may be then generated for each single word basedon character embeddings corresponding to the individual characters theword contains. In particular embodiments, the CNN model may calculateprobabilities for intents and slots associated with the user utterancebased on such input. In particular embodiments, the CNN model mayachieve competitive performance with significant efficiency andcompactness (100×) compared to conventional methods such as long-shortterm memory (LSTM) based models. Although this disclosure describesparticular natural-language understanding models by particular systemsin a particular manner, this disclosure contemplates any suitablenatural-language understanding model by any suitable system in anysuitable manner.

In particular embodiments, the assistant system executing locally on aclient system may receive, at the client system, a user input comprisingone or more words. Each word may comprise one or more characters. Theassistant system may then input the one or more words to a convolutionalneural network (CNN) model stored on the client system. In particularembodiments, the assistant system may access, from a data store of theclient system, a plurality of character-embeddings for a plurality ofcharacters, respectively. The assistant system may then generate, basedon the accessed character-embeddings, one or more word-embeddings forthe one or more words, respectively, by processing the accessedcharacter-embeddings with one or more convolutional layers and one ormore gated linear units of the CNN model. In particular embodiments, theassistant system may determine, based on an analysis of the one or moreword-embeddings by the CNN model, one or more tasks corresponding to theuser input for execution. The assistant system may further provide, atthe client system, an output responsive to the user input based on theexecution of the one or more tasks.

Certain technical challenges exist for learning a CNN model executing ona client system for natural-language understanding. One technicalchallenge may include accurately understanding a user input. Thesolution presented by the embodiments disclosed herein to address theabove challenge may be using gate linear units (GLUs) because GLUs maybe able to capture the importance of each word in the user input tolearn the context of the user input for accurate understanding. Anothertechnical challenge may include saving storage on the client system whenexecuting the CNN model. The solution presented by the embodimentsdisclosed herein to address this challenge may include storingcharacter-embeddings corresponding to different characters that may formdifferent words and accessing them when executing the CNN model sincethe character-embeddings take much less storage than word-embeddings ofall possible words. Another technical challenge may include making theCNN model compact and efficient. The solutions presented by theembodiments disclosed herein to address this challenge may include usingdigital signal processing algorithms, quantization, pruning algorithms,and sparsification algorithms for the CNN model as these techniques mayeffectively reduce the bit-rate the CNN model needs to handle, thenumber of layers of the CNN model, and the number of parameters of theCNN model, thereby achieving smaller model file size and latency.

Certain embodiments disclosed herein may provide one or more technicaladvantages. A technical advantage of the embodiments may includeperforming natural-language understanding on a local client system withperformance comparable to that on a remote server while additionallysaving file size and latency because the CNN model uses an architecturetailored for the limited computing power and storage of the clientsystem. Another technical advantage of the embodiments may includeeffective protection of user privacy as the natural-languageunderstanding is performed on the client system, which may guaranteethat all sensitive user data is processed locally on the client systemwithout being transmitted to a remote server or a third-party system.Certain embodiments disclosed herein may provide none, some, or all ofthe above technical advantages. One or more other technical advantagesmay be readily apparent to one skilled in the art in view of thefigures, descriptions, and claims of the present disclosure.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed herein.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with anassistant system.

FIG. 2 illustrates an example architecture of the assistant system.

FIG. 3 illustrates an example diagram flow of server-side processes ofthe assistant system.

FIG. 4 illustrates an example diagram flow of processing a user input bythe assistant system.

FIG. 5 illustrates an example architecture of the CNN model.

FIG. 6 illustrates an example method for responding to a user inputusing an on-device CNN model.

FIG. 7 illustrates an example social graph.

FIG. 8 illustrates an example view of an embedding space.

FIG. 9 illustrates an example artificial neural network.

FIG. 10 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS System Overview

FIG. 1 illustrates an example network environment 100 associated with anassistant system. Network environment 100 includes a client system 130,an assistant system 140, a social-networking system 160, and athird-party system 170 connected to each other by a network 110.Although FIG. 1 illustrates a particular arrangement of a client system130, an assistant system 140, a social-networking system 160, athird-party system 170, and a network 110, this disclosure contemplatesany suitable arrangement of a client system 130, an assistant system140, a social-networking system 160, a third-party system 170, and anetwork 110. As an example and not by way of limitation, two or more ofa client system 130, a social-networking system 160, an assistant system140, and a third-party system 170 may be connected to each otherdirectly, bypassing a network 110. As another example, two or more of aclient system 130, an assistant system 140, a social-networking system160, and a third-party system 170 may be physically or logicallyco-located with each other in whole or in part. Moreover, although FIG.1 illustrates a particular number of client systems 130, assistantsystems 140, social-networking systems 160, third-party systems 170, andnetworks 110, this disclosure contemplates any suitable number of clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110. As an example and not by wayof limitation, network environment 100 may include multiple clientsystems 130, assistant systems 140, social-networking systems 160,third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example andnot by way of limitation, one or more portions of a network 110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. A network 110 may include one or more networks110.

Links 150 may connect a client system 130, an assistant system 140, asocial-networking system 160, and a third-party system 170 to acommunication network 110 or to each other. This disclosure contemplatesany suitable links 150. In particular embodiments, one or more links 150include one or more wireline (such as for example Digital SubscriberLine (DSL) or Data Over Cable Service Interface Specification (DOCSIS)),wireless (such as for example Wi-Fi or Worldwide Interoperability forMicrowave Access (WiMAX)), or optical (such as for example SynchronousOptical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links.In particular embodiments, one or more links 150 each include an ad hocnetwork, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN,a MAN, a portion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 150, or a combination of two or more such links150. Links 150 need not necessarily be the same throughout a networkenvironment 100. One or more first links 150 may differ in one or morerespects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by a clientsystem 130. As an example and not by way of limitation, a client system130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, smart speaker, virtual reality(VR) headset, augment reality (AR) smart glasses, other suitableelectronic device, or any suitable combination thereof. In particularembodiments, the client system 130 may be a smart assistant device. Moreinformation on smart assistant devices may be found in U.S. patentapplication Ser. No. 15/949,011, filed 9 Apr. 2018, U.S. patentapplication Ser. No. 16/153,574, filed 5 Oct. 2018, U.S. Design patentapplication No. 29/631910, filed 3 Jan. 2018, U.S. Design patentapplication No. 29/631747, filed 2 Jan. 2018, U.S. Design patentApplication No. 29/631913, filed 3 Jan. 2018, and U.S. Design patentapplication No. 29/631914, filed 3 Jan. 2018, each of which isincorporated by reference. This disclosure contemplates any suitableclient systems 130. A client system 130 may enable a network user at aclient system 130 to access a network 110. A client system 130 mayenable its user to communicate with other users at other client systems130.

In particular embodiments, a client system 130 may include a web browser132, and may have one or more add-ons, plug-ins, or other extensions. Auser at a client system 130 may enter a Uniform Resource Locator (URL)or other address directing a web browser 132 to a particular server(such as server 162, or a server associated with a third-party system170), and the web browser 132 may generate a Hyper Text TransferProtocol (HTTP) request and communicate the HTTP request to server. Theserver may accept the HTTP request and communicate to a client system130 one or more Hyper Text Markup Language (HTML) files responsive tothe HTTP request. The client system 130 may render a web interface (e.g.a webpage) based on the HTML files from the server for presentation tothe user. This disclosure contemplates any suitable source files. As anexample and not by way of limitation, a web interface may be renderedfrom HTML files, Extensible Hyper Text Markup Language (XHTML) files, orExtensible Markup Language (XML) files, according to particular needs.Such interfaces may also execute scripts, combinations of markuplanguage and scripts, and the like. Herein, reference to a web interfaceencompasses one or more corresponding source files (which a browser mayuse to render the web interface) and vice versa, where appropriate.

In particular embodiments, a client system 130 may include asocial-networking application 134 installed on the client system 130. Auser at a client system 130 may use the social-networking application134 to access on online social network. The user at the client system130 may use the social-networking application 134 to communicate withthe user's social connections (e.g., friends, followers, followedaccounts, contacts, etc.). The user at the client system 130 may alsouse the social-networking application 134 to interact with a pluralityof content objects (e.g., posts, news articles, ephemeral content, etc.)on the online social network. As an example and not by way oflimitation, the user may browse trending topics and breaking news usingthe social-networking application 134.

In particular embodiments, a client system 130 may include an assistantapplication 136. A user at a client system 130 may use the assistantapplication 136 to interact with the assistant system 140. In particularembodiments, the assistant application 136 may comprise a stand-aloneapplication. In particular embodiments, the assistant application 136may be integrated into the social-networking application 134 or anothersuitable application (e.g., a messaging application). In particularembodiments, the assistant application 136 may be also integrated intothe client system 130, an assistant hardware device, or any othersuitable hardware devices. In particular embodiments, the assistantapplication 136 may be accessed via the web browser 132. In particularembodiments, the user may provide input via different modalities. As anexample and not by way of limitation, the modalities may include audio,text, image, video, motion, orientation, etc. The assistant application136 may communicate the user input to the assistant system 140. Based onthe user input, the assistant system 140 may generate responses. Theassistant system 140 may send the generated responses to the assistantapplication 136. The assistant application 136 may then present theresponses to the user at the client system 130. The presented responsesmay be based on different modalities such as audio, text, image, andvideo. As an example and not by way of limitation, the user may verballyask the assistant application 136 about the traffic information (i.e.,via an audio modality) by speaking into a microphone of the clientsystem 130. The assistant application 136 may then communicate therequest to the assistant system 140. The assistant system 140 mayaccordingly generate a response and send it back to the assistantapplication 136. The assistant application 136 may further present theresponse to the user in text and/or images on a display of the clientsystem 130.

In particular embodiments, an assistant system 140 may assist users toretrieve information from different sources. The assistant system 140may also assist user to request services from different serviceproviders. In particular embodiments, the assist system 140 may receivea user request for information or services via the assistant application136 in the client system 130. The assist system 140 may usenatural-language understanding to analyze the user request based onuser's profile and other relevant information. The result of theanalysis may comprise different entities associated with an onlinesocial network. The assistant system 140 may then retrieve informationor request services associated with these entities. In particularembodiments, the assistant system 140 may interact with thesocial-networking system 160 and/or third-party system 170 whenretrieving information or requesting services for the user. Inparticular embodiments, the assistant system 140 may generate apersonalized communication content for the user using natural-languagegenerating techniques. The personalized communication content maycomprise, for example, the retrieved information or the status of therequested services. In particular embodiments, the assistant system 140may enable the user to interact with it regarding the information orservices in a stateful and multi-turn conversation by usingdialog-management techniques. The functionality of the assistant system140 is described in more detail in the discussion of FIG. 2 below.

In particular embodiments, the social-networking system 160 may be anetwork-addressable computing system that can host an online socialnetwork. The social-networking system 160 may generate, store, receive,and send social-networking data, such as, for example, user profiledata, concept-profile data, social-graph information, or other suitabledata related to the online social network. The social-networking system160 may be accessed by the other components of network environment 100either directly or via a network 110. As an example and not by way oflimitation, a client system 130 may access the social-networking system160 using a web browser 132, or a native application associated with thesocial-networking system 160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via a network 110. Inparticular embodiments, the social-networking system 160 may include oneor more servers 162. Each server 162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 162. In particular embodiments, thesocial-networking system 160 may include one or more data stores 164.Data stores 164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 164 may beorganized according to specific data structures. In particularembodiments, each data store 164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 130, a social-networkingsystem 160, an assistant system 140, or a third-party system 170 tomanage, retrieve, modify, add, or delete, the information stored in datastore 164.

In particular embodiments, the social-networking system 160 may storeone or more social graphs in one or more data stores 164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. The social-networking system 160may provide users of the online social network the ability tocommunicate and interact with other users. In particular embodiments,users may join the online social network via the social-networkingsystem 160 and then add connections (e.g., relationships) to a number ofother users of the social-networking system 160 whom they want to beconnected to. Herein, the term “friend” may refer to any other user ofthe social-networking system 160 with whom a user has formed aconnection, association, or relationship via the social-networkingsystem 160.

In particular embodiments, the social-networking system 160 may provideusers with the ability to take actions on various types of items orobjects, supported by the social-networking system 160. As an exampleand not by way of limitation, the items and objects may include groupsor social networks to which users of the social-networking system 160may belong, events or calendar entries in which a user might beinterested, computer-based applications that a user may use,transactions that allow users to buy or sell items via the service,interactions with advertisements that a user may perform, or othersuitable items or objects. A user may interact with anything that iscapable of being represented in the social-networking system 160 or byan external system of a third-party system 170, which is separate fromthe social-networking system 160 and coupled to the social-networkingsystem 160 via a network 110.

In particular embodiments, the social-networking system 160 may becapable of linking a variety of entities. As an example and not by wayof limitation, the social-networking system 160 may enable users tointeract with each other as well as receive content from third-partysystems 170 or other entities, or to allow users to interact with theseentities through an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 170 may beoperated by a different entity from an entity operating thesocial-networking system 160. In particular embodiments, however, thesocial-networking system 160 and third-party systems 170 may operate inconjunction with each other to provide social-networking services tousers of the social-networking system 160 or third-party systems 170. Inthis sense, the social-networking system 160 may provide a platform, orbackbone, which other systems, such as third-party systems 170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects. In particularembodiments, a third-party content provider may use one or morethird-party agents to provide content objects and/or services. Athird-party agent may be an implementation that is hosted and executingon the third-party system 170.

In particular embodiments, the social-networking system 160 alsoincludes user-generated content objects, which may enhance a user'sinteractions with the social-networking system 160. User-generatedcontent may include anything a user can add, upload, send, or “post” tothe social-networking system 160. As an example and not by way oflimitation, a user communicates posts to the social-networking system160 from a client system 130. Posts may include data such as statusupdates or other textual data, location information, photos, videos,links, music or other similar data or media. Content may also be addedto the social-networking system 160 by a third-party through a“communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may includea variety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, the social-networking system 160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. The social-networkingsystem 160 may also include suitable components such as networkinterfaces, security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments, thesocial-networking system 160 may include one or more user-profile storesfor storing user profiles. A user profile may include, for example,biographic information, demographic information, behavioral information,social information, or other types of descriptive information, such aswork experience, educational history, hobbies or preferences, interests,affinities, or location. Interest information may include interestsrelated to one or more categories. Categories may be general orspecific. As an example and not by way of limitation, if a user “likes”an article about a brand of shoes the category may be the brand, or thegeneral category of “shoes” or “clothing.” A connection store may beused for storing connection information about users. The connectioninformation may indicate users who have similar or common workexperience, group memberships, hobbies, educational history, or are inany way related or share common attributes. The connection informationmay also include user-defined connections between different users andcontent (both internal and external). A web server may be used forlinking the social-networking system 160 to one or more client systems130 or one or more third-party systems 170 via a network 110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between the social-networking system 160and one or more client systems 130. An API-request server may allow, forexample, an assistant system 140 or a third-party system 170 to accessinformation from the social-networking system 160 by calling one or moreAPIs. An action logger may be used to receive communications from a webserver about a user's actions on or off the social-networking system160. In conjunction with the action log, a third-party-content-objectlog may be maintained of user exposures to third-party-content objects.A notification controller may provide information regarding contentobjects to a client system 130. Information may be pushed to a clientsystem 130 as notifications, or information may be pulled from a clientsystem 130 responsive to a request received from a client system 130.Authorization servers may be used to enforce one or more privacysettings of the users of the social-networking system 160. A privacysetting of a user determines how particular information associated witha user can be shared. The authorization server may allow users to opt into or opt out of having their actions logged by the social-networkingsystem 160 or shared with other systems (e.g., a third-party system170), such as, for example, by setting appropriate privacy settings.Third-party-content-object stores may be used to store content objectsreceived from third parties, such as a third-party system 170. Locationstores may be used for storing location information received from clientsystems 130 associated with users. Advertisement-pricing modules maycombine social information, the current time, location information, orother suitable information to provide relevant advertisements, in theform of notifications, to a user.

Assistant Systems

FIG. 2 illustrates an example architecture of an assistant system 140.In particular embodiments, the assistant system 140 may assist a user toobtain information or services. The assistant system 140 may enable theuser to interact with it with multi-modal user input (such as voice,text, image, video, motion) in stateful and multi-turn conversations toget assistance. As an example and not by way of limitation, theassistant system 140 may support both audio input (verbal) and nonverbalinput, such as vision, location, gesture, motion, or hybrid/multi-modalinput. The assistant system 140 may create and store a user profilecomprising both personal and contextual information associated with theuser. In particular embodiments, the assistant system 140 may analyzethe user input using natural-language understanding. The analysis may bebased on the user profile of the user for more personalized andcontext-aware understanding. The assistant system 140 may resolveentities associated with the user input based on the analysis. Inparticular embodiments, the assistant system 140 may interact withdifferent agents to obtain information or services that are associatedwith the resolved entities. The assistant system 140 may generate aresponse for the user regarding the information or services by usingnatural-language generation. Through the interaction with the user, theassistant system 140 may use dialog management techniques to manage andforward the conversation flow with the user. In particular embodiments,the assistant system 140 may further assist the user to effectively andefficiently digest the obtained information by summarizing theinformation. The assistant system 140 may also assist the user to bemore engaging with an online social network by providing tools that helpthe user interact with the online social network (e.g., creating posts,comments, messages). The assistant system 140 may additionally assistthe user to manage different tasks such as keeping track of events. Inparticular embodiments, the assistant system 140 may proactivelyexecute, without a user input, pre-authorized tasks that are relevant touser interests and preferences based on the user profile, at a timerelevant for the user. In particular embodiments, the assistant system140 may check privacy settings to ensure that accessing a user's profileor other user information and executing different tasks are permittedsubject to the user's privacy settings. More information on assistingusers subject to privacy settings may be found in U.S. patentapplication Ser. No. 16/182,542, filed 6 Nov. 2018, which isincorporated by reference.

In particular embodiments, the assistant system 140 may assist user viaa hybrid architecture built upon both client-side process andserver-side process. The client-side process and the server-side processmay be two parallel workflows for processing a user input and providingassistances to the user. In particular embodiments, the client-sideprocess may be performed locally on a client system 130 associated witha user. By contrast, the server-side process may be performed remotelyon one or more computing systems. In particular embodiments, anassistant orchestrator on the client system 130 may coordinate receivinguser input (e.g., audio signal) and determining whether to useclient-side process or server-side process or both to respond to theuser input. A dialog arbitrator may analyze the processing results fromeach process. The dialog arbitrator may instruct agents on theclient-side or server-side to execute tasks associated with the userinput based on the aforementioned analyses. The execution results may befurther rendered as output to the client system 130. By leveraging bothclient-side and server-side processes, the assistant system 140 caneffectively assist a user with optimal usage of computing resourceswhile at the same time protecting user privacy and enhancing security.

In particular embodiments, the assistant system 140 may receive a userinput from a client system 130 associated with the user. In particularembodiments, the user input may be a user-generated input that is sentto the assistant system 140 in a single turn. The user input may beverbal, nonverbal, or a combination thereof. As an example and not byway of limitation, the nonverbal user input may be based on the user'svoice, vision, location, activity, gesture, motion, or a combinationthereof. If the user input is based on the user's voice (e.g., the usermay speak to the client system 130), such user input may be firstprocessed by a system audio API 202 (application programming interface).The system audio API 202 may conduct echo cancellation, noise removal,beam forming, and self-user voice activation, speaker identification,voice activity detection (VAD), and any other acoustic techniques togenerate audio data that is readily processable by the assistant system140. In particular embodiments, the system audio API 202 may performwake-word detection 204 from the user input. As an example and not byway of limitation, a wake-word may be “hey assistant”. If such wake-wordis detected, the assistant system 140 may be activated accordingly. Inalternative embodiments, the user may activate the assistant system 140via a visual signal without a wake-word. The visual signal may bereceived at a low-power sensor (e.g., a camera) that can detect variousvisual signals. As an example and not by way of limitation, the visualsignal may be a barcode, a QR code or a universal product code (UPC)detected by the client system 130. As another example and not by way oflimitation, the visual signal may be the user's gaze at an object. Asyet another example and not by way of limitation, the visual signal maybe a user gesture, e.g., the user pointing at an object.

In particular embodiments, the audio data from the system audio API 202may be sent to an assistant orchestrator 206. The assistant orchestrator206 may be executing on the client system 130. In particularembodiments, the assistant orchestrator 206 may determine whether torespond to the user input by using a client-side process or aserver-side process. As indicated in FIG. 2, the client-side process isillustrated below the dashed line 207 whereas the server-side process isillustrated above the dashed line 207. The assistant orchestrator 206may also determine to respond to the user input by using both theclient-side process and the server-side process simultaneously. AlthoughFIG. 2 illustrates the assistant orchestrator 206 as being a client-sideprocess, the assistant orchestrator 206 may be a server-side process ormay be a hybrid process split between client- and server-side processes.

In particular embodiments, the server-side process may be as followsafter audio data is generated from the system audio API 202. Theassistant orchestrator 206 may send the audio data to a remote computingsystem that hosts different modules of the assistant system 140 torespond to the user input. In particular embodiments, the audio data maybe received at a remote automatic speech recognition (ASR) module 208.The ASR module 208 may allow a user to dictate and have speechtranscribed as written text, have a document synthesized as an audiostream, or issue commands that are recognized as such by the system. TheASR module 208 may use statistical models to determine the most likelysequences of words that correspond to a given portion of speech receivedby the assistant system 140 as audio input. The models may include oneor more of hidden Markov models, neural networks, deep learning models,or any combination thereof. The received audio input may be encoded intodigital data at a particular sampling rate (e.g., 16, 44.1, or 96 kHz)and with a particular number of bits representing each sample (e.g., 8,16, of 24 bits).

In particular embodiments, the ASR module 208 may comprise differentcomponents. The ASR module 208 may comprise one or more of agrapheme-to-phoneme (G2P) model, a pronunciation learning model, apersonalized acoustic model, a personalized language model (PLM), or anend-pointing model. In particular embodiments, the G2P model may be usedto determine a user's grapheme-to-phoneme style, e.g., what it may soundlike when a particular user speaks a particular word. The personalizedacoustic model may be a model of the relationship between audio signalsand the sounds of phonetic units in the language. Therefore, suchpersonalized acoustic model may identify how a user's voice sounds. Thepersonalized acoustical model may be generated using training data suchas training speech received as audio input and the correspondingphonetic units that correspond to the speech. The personalizedacoustical model may be trained or refined using the voice of aparticular user to recognize that user's speech. In particularembodiments, the personalized language model may then determine the mostlikely phrase that corresponds to the identified phonetic units for aparticular audio input. The personalized language model may be a modelof the probabilities that various word sequences may occur in thelanguage. The sounds of the phonetic units in the audio input may bematched with word sequences using the personalized language model, andgreater weights may be assigned to the word sequences that are morelikely to be phrases in the language. The word sequence having thehighest weight may be then selected as the text that corresponds to theaudio input. In particular embodiments, the personalized language modelmay be also used to predict what words a user is most likely to saygiven a context. In particular embodiments, the end-pointing model maydetect when the end of an utterance is reached.

In particular embodiments, the output of the ASR module 208 may be sentto a remote natural-language understanding (NLU) module 210. The NLUmodule 210 may perform named entity resolution (NER). The NLU module 210may additionally consider contextual information when analyzing the userinput. In particular embodiments, an intent and/or a slot may be anoutput of the NLU module 210. An intent may be an element in apre-defined taxonomy of semantic intentions, which may indicate apurpose of a user interacting with the assistant system 140. The NLUmodule 210 may classify a user input into a member of the pre-definedtaxonomy, e.g., for the input “Play Beethoven's 5th,” the NLU module 210may classify the input as having the intent [IN:play_music]. Inparticular embodiments, a domain may denote a social context ofinteraction, e.g., education, or a namespace for a set of intents, e.g.,music. A slot may be a named sub-string corresponding to a characterstring within the user input, representing a basic semantic entity. Forexample, a slot for “pizza” may be [SL:dish]. In particular embodiments,a set of valid or expected named slots may be conditioned on theclassified intent. As an example and not by way of limitation, for theintent [IN:play_music], a valid slot may be [SL:song_name]. Inparticular embodiments, the NLU module 210 may additionally extractinformation from one or more of a social graph, a knowledge graph, or aconcept graph, and retrieve a user's profile from one or more remotedata stores 212. The NLU module 210 may further process information fromthese different sources by determining what information to aggregate,annotating n-grams of the user input, ranking the n-grams withconfidence scores based on the aggregated information, and formulatingthe ranked n-grams into features that can be used by the NLU module 210for understanding the user input.

In particular embodiments, the NLU module 210 may identify one or moreof a domain, an intent, or a slot from the user input in a personalizedand context-aware manner. As an example and not by way of limitation, auser input may comprise “show me how to get to the coffee shop”. The NLUmodule 210 may identify the particular coffee shop that the user wantsto go based on the user's personal information and the associatedcontextual information. In particular embodiments, the NLU module 210may comprise a lexicon of a particular language and a parser and grammarrules to partition sentences into an internal representation. The NLUmodule 210 may also comprise one or more programs that perform naivesemantics or stochastic semantic analysis to the use of pragmatics tounderstand a user input. In particular embodiments, the parser may bebased on a deep learning architecture comprising multiple long-shortterm memory (LSTM) networks. As an example and not by way of limitation,the parser may be based on a recurrent neural network grammar (RNNG)model, which is a type of recurrent and recursive LSTM algorithm. Moreinformation on natural-language understanding may be found in U.S.patent application Ser. No. 16/011,062, filed 18 Jun. 2018, U.S. patentapplication Ser. No. 16/025,317, filed 2 Jul. 2018, and U.S. patentapplication Ser. No. 16/038,120, filed 17 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the output of the NLU module 210 may be sentto a remote reasoning module 214. The reasoning module 214 may comprisea dialog manager and an entity resolution component. In particularembodiments, the dialog manager may have complex dialog logic andproduct-related business logic. The dialog manager may manage the dialogstate and flow of the conversation between the user and the assistantsystem 140. The dialog manager may additionally store previousconversations between the user and the assistant system 140. Inparticular embodiments, the dialog manager may communicate with theentity resolution component to resolve entities associated with the oneor more slots, which supports the dialog manager to advance the flow ofthe conversation between the user and the assistant system 140. Inparticular embodiments, the entity resolution component may access oneor more of the social graph, the knowledge graph, or the concept graphwhen resolving the entities. Entities may include, for example, uniqueusers or concepts, each of which may have a unique identifier (ID). Asan example and not by way of limitation, the knowledge graph maycomprise a plurality of entities. Each entity may comprise a singlerecord associated with one or more attribute values. The particularrecord may be associated with a unique entity identifier. Each recordmay have diverse values for an attribute of the entity. Each attributevalue may be associated with a confidence probability. A confidenceprobability for an attribute value represents a probability that thevalue is accurate for the given attribute. Each attribute value may bealso associated with a semantic weight. A semantic weight for anattribute value may represent how the value semantically appropriate forthe given attribute considering all the available information. Forexample, the knowledge graph may comprise an entity of a book “Alice'sAdventures”, which includes information that has been extracted frommultiple content sources (e.g., an online social network, onlineencyclopedias, book review sources, media databases, and entertainmentcontent sources), and then deduped, resolved, and fused to generate thesingle unique record for the knowledge graph. The entity may beassociated with a “fantasy” attribute value which indicates the genre ofthe book “Alice's Adventures”. More information on the knowledge graphmay be found in U.S. patent application Ser. No. 16/048,049, filed 27Jul. 2018, and U.S. patent application Ser. No. 16/048,101, filed 27Jul. 2018, each of which is incorporated by reference.

In particular embodiments, the entity resolution component may check theprivacy constraints to guarantee that the resolving of the entities doesnot violate privacy policies. As an example and not by way oflimitation, an entity to be resolved may be another user who specifiesin his/her privacy settings that his/her identity should not besearchable on the online social network, and thus the entity resolutioncomponent may not return that user's identifier in response to arequest. Based on the information obtained from the social graph, theknowledge graph, the concept graph, and the user profile, and subject toapplicable privacy policies, the entity resolution component maytherefore resolve the entities associated with the user input in apersonalized, context-aware, and privacy-aware manner. In particularembodiments, each of the resolved entities may be associated with one ormore identifiers hosted by the social-networking system 160. As anexample and not by way of limitation, an identifier may comprise aunique user identifier (ID) corresponding to a particular user (e.g., aunique username or user ID number). In particular embodiments, each ofthe resolved entities may be also associated with a confidence score.More information on resolving entities may be found in U.S. patentapplication Ser. No. 16/048,049, filed 27 Jul. 2018, and U.S. patentapplication Ser. No. 16/048,072, filed 27 Jul. 2018, each of which isincorporated by reference.

In particular embodiments, the dialog manager may conduct dialogoptimization and assistant state tracking. Dialog optimization is theproblem of using data to understand what the most likely branching in adialog should be. As an example and not by way of limitation, withdialog optimization the assistant system 140 may not need to confirm whoa user wants to call because the assistant system 140 has highconfidence that a person inferred based on dialog optimization would bevery likely whom the user wants to call. In particular embodiments, thedialog manager may use reinforcement learning for dialog optimization.Assistant state tracking aims to keep track of a state that changes overtime as a user interacts with the world and the assistant system 140interacts with the user. As an example and not by way of limitation,assistant state tracking may track what a user is talking about, whomthe user is with, where the user is, what tasks are currently inprogress, and where the user's gaze is at, etc., subject to applicableprivacy policies. In particular embodiments, the dialog manager may usea set of operators to track the dialog state. The operators may comprisethe necessary data and logic to update the dialog state. Each operatormay act as delta of the dialog state after processing an incomingrequest. In particular embodiments, the dialog manager may furthercomprise a dialog state tracker and an action selector. In alternativeembodiments, the dialog state tracker may replace the entity resolutioncomponent and resolve the references/mentions and keep track of thestate.

In particular embodiments, the reasoning module 214 may further conductfalse trigger mitigation. The goal of false trigger mitigation is todetect false triggers (e.g., wake-word) of assistance requests and toavoid generating false records when a user actually does not intend toinvoke the assistant system 140. As an example and not by way oflimitation, the reasoning module 214 may achieve false triggermitigation based on a nonsense detector. If the nonsense detectordetermines that a wake-word makes no sense at this point in theinteraction with the user, the reasoning module 214 may determine thatinferring the user intended to invoke the assistant system 140 may beincorrect. In particular embodiments, the output of the reasoning module214 may be sent a remote dialog arbitrator 216.

In particular embodiments, each of the ASR module 208, NLU module 210,and reasoning module 214 may access the remote data store 212, whichcomprises user episodic memories to determine how to assist a user moreeffectively. More information on episodic memories may be found in U.S.patent application Ser. No. 16/552,559, filed 27 Aug. 2019, which isincorporated by reference. The data store 212 may additionally store theuser profile of the user. The user profile of the user may comprise userprofile data including demographic information, social information, andcontextual information associated with the user. The user profile datamay also include user interests and preferences on a plurality oftopics, aggregated through conversations on news feed, search logs,messaging platforms, etc. The usage of a user profile may be subject toprivacy constraints to ensure that a user's information can be used onlyfor his/her benefit, and not shared with anyone else. More informationon user profiles may be found in U.S. patent application Ser. No.15/967,239, filed 30 Apr. 2018, which is incorporated by reference.

In particular embodiments, parallel to the aforementioned server-sideprocess involving the ASR module 208, NLU module 210, and reasoningmodule 214, the client-side process may be as follows. In particularembodiments, the output of the assistant orchestrator 206 may be sent toa local ASR module 216 on the client system 130. The ASR module 216 maycomprise a personalized language model (PLM), a G2P model, and anend-pointing model. Because of the limited computing power of the clientsystem 130, the assistant system 140 may optimize the personalizedlanguage model at run time during the client-side process. As an exampleand not by way of limitation, the assistant system 140 may pre-compute aplurality of personalized language models for a plurality of possiblesubjects a user may talk about. When a user requests assistance, theassistant system 140 may then swap these pre-computed language modelsquickly so that the personalized language model may be optimized locallyby the assistant system 140 at run time based on user activities. As aresult, the assistant system 140 may have a technical advantage ofsaving computational resources while efficiently determining what theuser may be talking about. In particular embodiments, the assistantsystem 140 may also re-learn user pronunciations quickly at run time.

In particular embodiments, the output of the ASR module 216 may be sentto a local NLU module 218. In particular embodiments, the NLU module 218herein may be more compact compared to the remote NLU module 210supported on the server-side. When the ASR module 216 and NLU module 218process the user input, they may access a local assistant memory 220.The local assistant memory 220 may be different from the user memoriesstored on the data store 212 for the purpose of protecting user privacy.In particular embodiments, the local assistant memory 220 may be syncingwith the user memories stored on the data store 212 via the network 110.As an example and not by way of limitation, the local assistant memory220 may sync a calendar on a user's client system 130 with a server-sidecalendar associate with the user. In particular embodiments, any secureddata in the local assistant memory 220 may be only accessible to themodules of the assistant system 140 that are locally executing on theclient system 130.

In particular embodiments, the output of the NLU module 218 may be sentto a local reasoning module 222. The reasoning module 222 may comprise adialog manager and an entity resolution component. Due to the limitedcomputing power, the reasoning module 222 may conduct on-device learningthat is based on learning algorithms particularly tailored for clientsystems 130. As an example and not by way of limitation, federatedlearning may be used by the reasoning module 222. Federated learning isa specific category of distributed machine learning approaches whichtrains machine learning models using decentralized data residing on enddevices such as mobile phones. In particular embodiments, the reasoningmodule 222 may use a particular federated learning model, namelyfederated user representation learning, to extend existingneural-network personalization techniques to federated learning.Federated user representation learning can personalize models infederated learning by learning task-specific user representations (i.e.,embeddings) or by personalizing model weights. Federated userrepresentation learning is a simple, scalable, privacy-preserving, andresource-efficient. Federated user representation learning may dividemodel parameters into federated and private parameters. Privateparameters, such as private user embeddings, may be trained locally on aclient system 130 instead of being transferred to or averaged on aremote server. Federated parameters, by contrast, may be trainedremotely on the server. In particular embodiments, the reasoning module222 may use another particular federated learning model, namely activefederated learning to transmit a global model trained on the remoteserver to client systems 130 and calculate gradients locally on theseclient systems 130. Active federated learning may enable the reasoningmodule to minimize the transmission costs associated with downloadingmodels and uploading gradients. For active federated learning, in eachround client systems are selected not uniformly at random, but with aprobability conditioned on the current model and the data on the clientsystems to maximize efficiency. In particular embodiments, the reasoningmodule 222 may use another particular federated learning model, namelyfederated Adam. Conventional federated learning model may use stochasticgradient descent (SGD) optimizers. By contrast, the federated Adam modelmay use moment-based optimizers. Instead of using the averaged modeldirectly as what conventional work does, federated Adam model may usethe averaged model to compute approximate gradients. These gradients maybe then fed into the federated Adam model, which may de-noise stochasticgradients and use a per-parameter adaptive learning rate. Gradientsproduced by federated learning may be even noisier than stochasticgradient descent (because data may be not independent and identicallydistributed), so federated Adam model may help even more deal with thenoise. The federated Adam model may use the gradients to take smartersteps towards minimizing the objective function. The experiments showthat conventional federated learning on a benchmark has 1.6% drop in ROC(Receiver Operating Characteristics) curve whereas federated Adam modelhas only 0.4% drop. In addition, federated Adam model has no increase incommunication or on-device computation. In particular embodiments, thereasoning module 222 may also perform false trigger mitigation. Thisfalse trigger mitigation may help detect false activation requests,e.g., wake-word, on the client system 130 when the user's speech inputcomprises data that is subject to privacy constraints. As an example andnot by way of limitation, when a user is in a voice call, the user'sconversation is private and the false trigger detection based on suchconversation can only occur locally on the user's client system 130.

In particular embodiments, the assistant system 140 may comprise a localcontext engine 224. The context engine 224 may process all the otheravailable signals to provide more informative cues to the reasoningmodule 222. As an example and not by way of limitation, the contextengine 224 may have information related to people, sensory data fromclient system 130 sensors (e.g., microphone, camera) that are furtheranalyzed by computer vision technologies, geometry constructions,activity data, inertial data (e.g., collected by a VR headset),location, etc. In particular embodiments, the computer visiontechnologies may comprise human skeleton reconstruction, face detection,facial recognition, hand tracking, eye tracking, etc. In particularembodiments, geometry constructions may comprise constructing objectssurrounding a user using data collected by a client system 130. As anexample and not by way of limitation, the user may be wearing AR glassesand geometry construction may aim to determine where the floor is, wherethe wall is, where the user's hands are, etc. In particular embodiments,inertial data may be data associated with linear and angular motions. Asan example and not by way of limitation, inertial data may be capturedby AR glasses which measures how a user's body parts move.

In particular embodiments, the output of the local reasoning module 222may be sent to the dialog arbitrator 216. The dialog arbitrator 216 mayfunction differently in three scenarios. In the first scenario, theassistant orchestrator 206 determines to use server-side process, forwhich the dialog arbitrator 216 may transmit the output of the reasoningmodule 214 to a remote action execution module 226. In the secondscenario, the assistant orchestrator 206 determines to use bothserver-side process and client-side process, for which the dialogarbitrator 216 may aggregate output from both reasoning modules (i.e.,remote reasoning module 214 and local reasoning module 222) of bothprocesses and analyze them. As an example and not by way of limitation,the dialog arbitrator 216 may perform ranking and select the bestreasoning result for responding to the user input. In particularembodiments, the dialog arbitrator 216 may further determine whether touse agents on the server-side or on the client-side to execute relevanttasks based on the analysis. In the third scenario, the assistantorchestrator 206 determines to use client-side process and the dialogarbitrator 216 needs to evaluate the output of the local reasoningmodule 222 to determine if the client-side process can complete the taskof handling the user input.

In particular embodiments, for the first and second scenarios mentionedabove, the dialog arbitrator 216 may determine that the agents on theserver-side are necessary to execute tasks responsive to the user input.Accordingly, the dialog arbitrator 216 may send necessary informationregarding the user input to the action execution module 226. The actionexecution module 226 may call one or more agents to execute the tasks.In alternative embodiments, the action selector of the dialog managermay determine actions to execute and instruct the action executionmodule 226 accordingly. In particular embodiments, an agent may be animplementation that serves as a broker across a plurality of contentproviders for one domain. A content provider may be an entityresponsible for carrying out an action associated with an intent orcompleting a task associated with the intent. In particular embodiments,the agents may comprise first-party agents and third-party agents. Inparticular embodiments, first-party agents may comprise internal agentsthat are accessible and controllable by the assistant system 140 (e.g.agents associated with services provided by the online social network,such as messaging services or photo-share services). In particularembodiments, third-party agents may comprise external agents that theassistant system 140 has no control over (e.g., third-party online musicapplication agents, ticket sales agents). The first-party agents may beassociated with first-party providers that provide content objectsand/or services hosted by the social-networking system 160. Thethird-party agents may be associated with third-party providers thatprovide content objects and/or services hosted by the third-party system170. In particular embodiments, each of the first-party agents orthird-party agents may be designated for a particular domain. As anexample and not by way of limitation, the domain may comprise weather,transportation, music, etc. In particular embodiments, the assistantsystem 140 may use a plurality of agents collaboratively to respond to auser input. As an example and not by way of limitation, the user inputmay comprise “direct me to my next meeting.” The assistant system 140may use a calendar agent to retrieve the location of the next meeting.The assistant system 140 may then use a navigation agent to direct theuser to the next meeting.

In particular embodiments, for the second and third scenarios mentionedabove, the dialog arbitrator 216 may determine that the agents on theclient-side are capable of executing tasks responsive to the user inputbut additional information is needed (e.g., response templates) or thatthe tasks can be only handled by the agents on the server-side. If thedialog arbitrator 216 determines that the tasks can be only handled bythe agents on the server-side, the dialog arbitrator 216 may sendnecessary information regarding the user input to the action executionmodule 226. If the dialog arbitrator 216 determines that the agents onthe client-side are capable of executing tasks but response templatesare needed, the dialog arbitrator 216 may send necessary informationregarding the user input to a remote response template generation module228. The output of the response template generation module 228 may befurther sent to a local action execution module 230 executing on theclient system 130.

In particular embodiments, the action execution module 230 may calllocal agents to execute tasks. A local agent on the client system 130may be able to execute simpler tasks compared to an agent on theserver-side. As an example and not by way of limitation, multipledevice-specific implementations (e.g., real-time calls for a clientsystem 130 or a messaging application on the client system 130) may behandled internally by a single agent. Alternatively, thesedevice-specific implementations may be handled by multiple agentsassociated with multiple domains. In particular embodiments, the actionexecution module 230 may additionally perform a set of generalexecutable dialog actions. The set of executable dialog actions mayinteract with agents, users and the assistant system 140 itself. Thesedialog actions may comprise dialog actions for slot request,confirmation, disambiguation, agent execution, etc. The dialog actionsmay be independent of the underlying implementation of the actionselector or dialog policy. Both tree-based policy and model-based policymay generate the same basic dialog actions, with a callback functionhiding any action selector specific implementation details.

In particular embodiments, the output from the remote action executionmodule 226 on the server-side may be sent to a remote response executionmodule 232. In particular embodiments, the action execution module 226may communicate back to the dialog arbitrator 216 for more information.The response execution module 232 may be based on a remoteconversational understanding (CU) composer. In particular embodiments,the output from the action execution module 226 may be formulated as a<k, c, u, d> tuple, in which k indicates a knowledge source, c indicatesa communicative goal, u indicates a user model, and d indicates adiscourse model. In particular embodiments, the CU composer may comprisea natural-language generator (NLG) and a user interface (UI) payloadgenerator. The natural-language generator may generate a communicationcontent based on the output of the action execution module 226 usingdifferent language models and/or language templates. In particularembodiments, the generation of the communication content may beapplication specific and also personalized for each user. The CUcomposer may also determine a modality of the generated communicationcontent using the UI payload generator. In particular embodiments, theNLG may comprise a content determination component, a sentence planner,and a surface realization component. The content determination componentmay determine the communication content based on the knowledge source,communicative goal, and the user's expectations. As an example and notby way of limitation, the determining may be based on a descriptionlogic. The description logic may comprise, for example, threefundamental notions which are individuals (representing objects in thedomain), concepts (describing sets of individuals), and roles(representing binary relations between individuals or concepts). Thedescription logic may be characterized by a set of constructors thatallow the natural-language generator to build complex concepts/rolesfrom atomic ones. In particular embodiments, the content determinationcomponent may perform the following tasks to determine the communicationcontent. The first task may comprise a translation task, in which theinput to the natural-language generator may be translated to concepts.The second task may comprise a selection task, in which relevantconcepts may be selected among those resulted from the translation taskbased on the user model. The third task may comprise a verificationtask, in which the coherence of the selected concepts may be verified.The fourth task may comprise an instantiation task, in which theverified concepts may be instantiated as an executable file that can beprocessed by the natural-language generator. The sentence planner maydetermine the organization of the communication content to make it humanunderstandable. The surface realization component may determine specificwords to use, the sequence of the sentences, and the style of thecommunication content. The UI payload generator may determine apreferred modality of the communication content to be presented to theuser. In particular embodiments, the CU composer may check privacyconstraints associated with the user to make sure the generation of thecommunication content follows the privacy policies. More information onnatural-language generation may be found in U.S. patent application Ser.No. 15/967,279, filed 30 Apr. 2018, and U.S. patent application Ser. No.15/966,455, filed 30 Apr. 2018, each of which is incorporated byreference.

In particular embodiments, the output from the local action executionmodule 230 on the client system 130 may be sent to a local responseexecution module 234. The response execution module 234 may be based ona local conversational understanding (CU) composer. The CU composer maycomprise a natural-language generation (NLG) module. As the computingpower of a client system 130 may be limited, the NLG module may besimple for the consideration of computational efficiency. Because theNLG module may be simple, the output of the response execution module234 may be sent to a local response expansion module 236. The responseexpansion module 236 may further expand the result of the responseexecution module 234 to make a response more natural and contain richersemantic information.

In particular embodiments, if the user input is based on audio signals,the output of the response execution module 232 on the server-side maybe sent to a remote text-to-speech (TTS) module 238. Similarly, theoutput of the response expansion module 236 on the client-side may besent to a local TTS module 240. Both TTS modules may convert a responseto audio signals. In particular embodiments, the output from theresponse execution module 232, the response expansion module 236, or theTTS modules on both sides, may be finally sent to a local render outputmodule 242. The render output module 242 may generate a response that issuitable for the client system 130. As an example and not by way oflimitation, the output of the response execution module 232 or theresponse expansion module 236 may comprise one or more ofnatural-language strings, speech, actions with parameters, or renderedimages or videos that can be displayed in a VR headset or AR smartglasses. As a result, the render output module 242 may determine whattasks to perform based on the output of CU composer to render theresponse appropriately for displaying on the VR headset or AR smartglasses. For example, the response may be visual-based modality (e.g.,an image or a video clip) that can be displayed via the VR headset or ARsmart glasses. As another example, the response may be audio signalsthat can be played by the user via VR headset or AR smart glasses. Asyet another example, the response may be augmented-reality data that canbe rendered VR headset or AR smart glasses for enhancing userexperience.

In particular embodiments, the assistant system 140 may have a varietyof capabilities including audio cognition, visual cognition, signalsintelligence, reasoning, and memories. In particular embodiments, thecapability of audio recognition may enable the assistant system 140 tounderstand a user's input associated with various domains in differentlanguages, understand a conversation and be able to summarize it,perform on-device audio cognition for complex commands, identify a userby voice, extract topics from a conversation and auto-tag sections ofthe conversation, enable audio interaction without a wake-word, filterand amplify user voice from ambient noise and conversations, understandwhich client system 130 (if multiple client systems 130 are in vicinity)a user is talking to.

In particular embodiments, the capability of visual cognition may enablethe assistant system 140 to perform face detection and tracking,recognize a user, recognize most people of interest in majormetropolitan areas at varying angles, recognize majority of interestingobjects in the world through a combination of existing machine-learningmodels and one-shot learning, recognize an interesting moment andauto-capture it, achieve semantic understanding over multiple visualframes across different episodes of time, provide platform support foradditional capabilities in people, places, objects recognition,recognize full set of settings and micro-locations includingpersonalized locations, recognize complex activities, recognize complexgestures to control a client system 130, handle images/videos fromegocentric cameras (e.g., with motion, capture angles, resolution,etc.), accomplish similar level of accuracy and speed regarding imageswith lower resolution, conduct one-shot registration and recognition ofpeople, places, and objects, and perform visual recognition on a clientsystem 130.

In particular embodiments, the assistant system 140 may leveragecomputer vision techniques to achieve visual cognition. Besides computervision techniques, the assistant system 140 may explore options that cansupplement these techniques to scale up the recognition of objects. Inparticular embodiments, the assistant system 140 may use supplementalsignals such as optical character recognition (OCR) of an object'slabels, GPS signals for places recognition, signals from a user's clientsystem 130 to identify the user. In particular embodiments, theassistant system 140 may perform general scene recognition (home, work,public space, etc.) to set context for the user and reduce thecomputer-vision search space to identify top likely objects or people.In particular embodiments, the assistant system 140 may guide users totrain the assistant system 140. For example, crowdsourcing may be usedto get users to tag and help the assistant system 140 recognize moreobjects over time. As another example, users can register their personalobjects as part of initial setup when using the assistant system 140.The assistant system 140 may further allow users to providepositive/negative signals for objects they interact with to train andimprove personalized models for them.

In particular embodiments, the capability of signals intelligence mayenable the assistant system 140 to determine user location, understanddate/time, determine family locations, understand users' calendars andfuture desired locations, integrate richer sound understanding toidentify setting/context through sound alone, build signals intelligencemodels at run time which may be personalized to a user's individualroutines.

In particular embodiments, the capability of reasoning may enable theassistant system 140 to have the ability to pick up any previousconversation threads at any point in the future, synthesize all signalsto understand micro and personalized context, learn interaction patternsand preferences from users' historical behavior and accurately suggestinteractions that they may value, generate highly predictive proactivesuggestions based on micro-context understanding, understand whatcontent a user may want to see at what time of a day, understand thechanges in a scene and how that may impact the user's desired content.

In particular embodiments, the capabilities of memories may enable theassistant system 140 to remember which social connections a userpreviously called or interacted with, write into memory and query memoryat will (i.e., open dictation and auto tags), extract richer preferencesbased on prior interactions and long-term learning, remember a user'slife history, extract rich information from egocentric streams of dataand auto catalog, and write to memory in structured form to form richshort, episodic and long-term memories.

FIG. 3 illustrates an example diagram flow of server-side processes ofthe assistant system 140. In particular embodiments, a server-assistantservice module 301 may access a request manager 302 upon receiving auser request. In alternative embodiments, the user request may be firstprocessed by the remote ASR module 208 if the user request is based onaudio signals. In particular embodiments, the request manager 302 maycomprise a context extractor 303 and a conversational understandingobject generator (CU object generator) 304. The context extractor 303may extract contextual information associated with the user request. Thecontext extractor 303 may also update contextual information based onthe assistant application 136 executing on the client system 130. As anexample and not by way of limitation, the update of contextualinformation may comprise content items are displayed on the clientsystem 130. As another example and not by way of limitation, the updateof contextual information may comprise whether an alarm is set on theclient system 130. As another example and not by way of limitation, theupdate of contextual information may comprise whether a song is playingon the client system 130. The CU object generator 304 may generateparticular content objects relevant to the user request. The contentobjects may comprise dialog-session data and features associated withthe user request, which may be shared with all the modules of theassistant system 140. In particular embodiments, the request manager 302may store the contextual information and the generated content objectsin data store 212 which is a particular data store implemented in theassistant system 140.

In particular embodiments, the request manger 302 may send the generatedcontent objects to the remote NLU module 210. The NLU module 210 mayperform a plurality of steps to process the content objects. At step305, the NLU module 210 may generate a whitelist for the contentobjects. In particular embodiments, the whitelist may compriseinterpretation data matching the user request. At step 306, the NLUmodule 210 may perform a featurization based on the whitelist. At step307, the NLU module 210 may perform domain classification/selection onuser request based on the features resulted from the featurization toclassify the user request into predefined domains. The domainclassification/selection results may be further processed based on tworelated procedures. At step 308 a, the NLU module 210 may process thedomain classification/selection result using an intent classifier. Theintent classifier may determine the user's intent associated with theuser request. In particular embodiments, there may be one intentclassifier for each domain to determine the most possible intents in agiven domain. As an example and not by way of limitation, the intentclassifier may be based on a machine-learning model that may take thedomain classification/selection result as input and calculate aprobability of the input being associated with a particular predefinedintent. At step 308 b, the NLU module 210 may process the domainclassification/selection result using a meta-intent classifier. Themeta-intent classifier may determine categories that describe the user'sintent. In particular embodiments, intents that are common to multipledomains may be processed by the meta-intent classifier. As an exampleand not by way of limitation, the meta-intent classifier may be based ona machine-learning model that may take the domainclassification/selection result as input and calculate a probability ofthe input being associated with a particular predefined meta-intent. Atstep 309 a, the NLU module 210 may use a slot tagger to annotate one ormore slots associated with the user request. In particular embodiments,the slot tagger may annotate the one or more slots for the n-grams ofthe user request. At step 309 b, the NLU module 210 may use a meta slottagger to annotate one or more slots for the classification result fromthe meta-intent classifier. In particular embodiments, the meta slottagger may tag generic slots such as references to items (e.g., thefirst), the type of slot, the value of the slot, etc. As an example andnot by way of limitation, a user request may comprise “change 500dollars in my account to Japanese yen.” The intent classifier may takethe user request as input and formulate it into a vector. The intentclassifier may then calculate probabilities of the user request beingassociated with different predefined intents based on a vectorcomparison between the vector representing the user request and thevectors representing different predefined intents. In a similar manner,the slot tagger may take the user request as input and formulate eachword into a vector. The intent classifier may then calculateprobabilities of each word being associated with different predefinedslots based on a vector comparison between the vector representing theword and the vectors representing different predefined slots. The intentof the user may be classified as “changing money”. The slots of the userrequest may comprise “500”, “dollars”, “account”, and “Japanese yen”.The meta-intent of the user may be classified as “financial service”.The meta slot may comprise “finance”.

In particular embodiments, the NLU module 210 may comprise a semanticinformation aggregator 310. The semantic information aggregator 310 mayhelp the NLU module 210 improve the domain classification/selection ofthe content objects by providing semantic information. In particularembodiments, the semantic information aggregator 310 may aggregatesemantic information in the following way. The semantic informationaggregator 310 may first retrieve information from a user context engine315. In particular embodiments, the user context engine 315 may compriseoffline aggregators and an online inference service. The offlineaggregators may process a plurality of data associated with the userthat are collected from a prior time window. As an example and not byway of limitation, the data may include news feed posts/comments,interactions with news feed posts/comments, search history, etc., thatare collected during a predetermined timeframe (e.g., from a prior90-day window). The processing result may be stored in the user contextengine 315 as part of the user profile. The online inference service mayanalyze the conversational data associated with the user that arereceived by the assistant system 140 at a current time. The analysisresult may be stored in the user context engine 315 also as part of theuser profile. In particular embodiments, both the offline aggregatorsand online inference service may extract personalization features fromthe plurality of data. The extracted personalization features may beused by other modules of the assistant system 140 to better understanduser input. In particular embodiments, the semantic informationaggregator 310 may then process the retrieved information, i.e., a userprofile, from the user context engine 315 in the following steps. Atstep 311, the semantic information aggregator 310 may process theretrieved information from the user context engine 315 based onnatural-language processing (NLP). In particular embodiments, thesemantic information aggregator 310 may tokenize text by textnormalization, extract syntax features from text, and extract semanticfeatures from text based on NLP. The semantic information aggregator 310may additionally extract features from contextual information, which isaccessed from dialog history between a user and the assistant system140. The semantic information aggregator 310 may further conduct globalword embedding, domain-specific embedding, and/or dynamic embeddingbased on the contextual information. At step 312, the processing resultmay be annotated with entities by an entity tagger. Based on theannotations, the semantic information aggregator 310 may generatedictionaries for the retrieved information at step 313. In particularembodiments, the dictionaries may comprise global dictionary featureswhich can be updated dynamically offline. At step 314, the semanticinformation aggregator 310 may rank the entities tagged by the entitytagger. In particular embodiments, the semantic information aggregator310 may communicate with different graphs 320 including one or more ofthe social graph, the knowledge graph, or the concept graph to extractontology data that is relevant to the retrieved information from theuser context engine 315. In particular embodiments, the semanticinformation aggregator 310 may aggregate the user profile, the rankedentities, and the information from the graphs 320. The semanticinformation aggregator 310 may then provide the aggregated informationto the NLU module 210 to facilitate the domain classification/selection.

In particular embodiments, the output of the NLU module 210 may be sentto the remote reasoning module 214. The reasoning module 214 maycomprise a co-reference component 325, an entity resolution component330, and a dialog manager 335. The output of the NLU module 210 may befirst received at the co-reference component 325 to interpret referencesof the content objects associated with the user request. In particularembodiments, the co-reference component 325 may be used to identify anitem to which the user request refers. The co-reference component 325may comprise reference creation 326 and reference resolution 327. Inparticular embodiments, the reference creation 326 may create referencesfor entities determined by the NLU module 210. The reference resolution327 may resolve these references accurately. As an example and not byway of limitation, a user request may comprise “find me the nearestgrocery store and direct me there”. The co-reference component 325 mayinterpret “there” as “the nearest grocery store”. In particularembodiments, the co-reference component 325 may access the user contextengine 315 and the dialog manager 335 when necessary to interpretreferences with improved accuracy.

In particular embodiments, the identified domains, intents,meta-intents, slots, and meta slots, along with the resolved referencesmay be sent to the entity resolution component 330 to resolve relevantentities. The entity resolution component 330 may execute generic anddomain-specific entity resolution. In particular embodiments, the entityresolution component 330 may comprise domain entity resolution 331 andgeneric entity resolution 332. The domain entity resolution 331 mayresolve the entities by categorizing the slots and meta slots intodifferent domains. In particular embodiments, entities may be resolvedbased on the ontology data extracted from the graphs 320. The ontologydata may comprise the structural relationship between differentslots/meta-slots and domains. The ontology may also comprise informationof how the slots/meta-slots may be grouped, related within a hierarchywhere the higher level comprises the domain, and subdivided according tosimilarities and differences. The generic entity resolution 332 mayresolve the entities by categorizing the slots and meta slots intodifferent generic topics. In particular embodiments, the resolving maybe also based on the ontology data extracted from the graphs 320. Theontology data may comprise the structural relationship between differentslots/meta-slots and generic topics. The ontology may also compriseinformation of how the slots/meta-slots may be grouped, related within ahierarchy where the higher level comprises the topic, and subdividedaccording to similarities and differences. As an example and not by wayof limitation, in response to the input of an inquiry of the advantagesof a particular brand of electric car, the generic entity resolution 332may resolve the referenced brand of electric car as vehicle and thedomain entity resolution 331 may resolve the referenced brand ofelectric car as electric car.

In particular embodiments, the output of the entity resolution component330 may be sent to the dialog manager 335 to advance the flow of theconversation with the user. The dialog manager 335 may be anasynchronous state machine that repeatedly updates the state and selectsactions based on the new state. The dialog manager 335 may comprisedialog intent resolution 336 and dialog state tracker 337. In particularembodiments, the dialog manager 335 may execute the selected actions andthen call the dialog state tracker 337 again until the action selectedrequires a user response, or there are no more actions to execute. Eachaction selected may depend on the execution result from previousactions. In particular embodiments, the dialog intent resolution 336 mayresolve the user intent associated with the current dialog session basedon dialog history between the user and the assistant system 140. Thedialog intent resolution 336 may map intents determined by the NLUmodule 210 to different dialog intents. The dialog intent resolution 336may further rank dialog intents based on signals from the NLU module210, the entity resolution component 330, and dialog history between theuser and the assistant system 140. In particular embodiments, instead ofdirectly altering the dialog state, the dialog state tracker 337 may bea side-effect free component and generate n-best candidates of dialogstate update operators that propose updates to the dialog state. Thedialog state tracker 337 may comprise intent resolvers containing logicto handle different types of NLU intent based on the dialog state andgenerate the operators. In particular embodiments, the logic may beorganized by intent handler, such as a disambiguation intent handler tohandle the intents when the assistant system 140 asks fordisambiguation, a confirmation intent handler that comprises the logicto handle confirmations, etc. Intent resolvers may combine the turnintent together with the dialog state to generate the contextual updatesfor a conversation with the user. A slot resolution component may thenrecursively resolve the slots in the update operators with resolutionproviders including the knowledge graph and domain agents. In particularembodiments, the dialog state tracker 337 may update/rank the dialogstate of the current dialog session. As an example and not by way oflimitation, the dialog state tracker 337 may update the dialog state as“completed” if the dialog session is over. As another example and not byway of limitation, the dialog state tracker 337 may rank the dialogstate based on a priority associated with it.

In particular embodiments, the reasoning module 214 may communicate withthe remote action execution module 226 and the dialog arbitrator 216,respectively. In particular embodiments, the dialog manager 335 of thereasoning module 214 may communicate with a task completion component340 of the action execution module 226 about the dialog intent andassociated content objects. In particular embodiments, the taskcompletion module 340 may rank different dialog hypotheses for differentdialog intents. The task completion module 340 may comprise an actionselector 341. In alternative embodiments, the action selector 341 may becomprised in the dialog manager 335. In particular embodiments, thedialog manager 335 may additionally check against dialog policies 345comprised in the dialog arbitrator 216 regarding the dialog state. Inparticular embodiments, a dialog policy 345 may comprise a datastructure that describes an execution plan of an action by an agent 350.The dialog policy 345 may comprise a general policy 346 and taskpolicies 347. In particular embodiments, the general policy 346 may beused for actions that are not specific to individual tasks. The generalpolicy 346 may comprise handling low confidence intents, internalerrors, unacceptable user response with retries, skipping or insertingconfirmation based on ASR or NLU confidence scores, etc. The generalpolicy 346 may also comprise the logic of ranking dialog state updatecandidates from the dialog state tracker 337 output and pick the one toupdate (such as picking the top ranked task intent). In particularembodiments, the assistant system 140 may have a particular interfacefor the general policy 346, which allows for consolidating scatteredcross-domain policy/business-rules, especial those found in the dialogstate tracker 337, into a function of the action selector 341. Theinterface for the general policy 346 may also allow for authoring ofself-contained sub-policy units that may be tied to specific situationsor clients, e.g., policy functions that may be easily switched on or offbased on clients, situation, etc. The interface for the general policy346 may also allow for providing a layering of policies with back-off,i.e. multiple policy units, with highly specialized policy units thatdeal with specific situations being backed up by more general policies346 that apply in wider circumstances. In this context the generalpolicy 346 may alternatively comprise intent or task specific policy. Inparticular embodiments, a task policy 347 may comprise the logic foraction selector 341 based on the task and current state. In particularembodiments, there may be the following four types of task policies347: 1) manually crafted tree-based dialog plans; 2) coded policy thatdirectly implements the interface for generating actions; 3)configurator-specified slot-filling tasks; and 4) machine-learning modelbased policy learned from data. In particular embodiments, the assistantsystem 140 may bootstrap new domains with rule-based logic and laterrefine the task policies 347 with machine-learning models. In particularembodiments, a dialog policy 345 may a tree-based policy, which is apre-constructed dialog plan. Based on the current dialog state, a dialogpolicy 345 may choose a node to execute and generate the correspondingactions. As an example and not by way of limitation, the tree-basedpolicy may comprise topic grouping nodes and dialog action (leaf) nodes.

In particular embodiments, the action selector 341 may take candidateoperators of dialog state and consult the dialog policy 345 to decidewhat action should be executed. The assistant system 140 may use ahierarchical dialog policy with general policy 346 handling thecross-domain business logic and task policies 347 handles thetask/domain specific logic. In particular embodiments, the generalpolicy 346 may pick one operator from the candidate operators to updatethe dialog state, followed by the selection of a user facing action by atask policy 347. Once a task is active in the dialog state, thecorresponding task policy 347 may be consulted to select right actions.In particular embodiments, both the dialog state tracker 337 and theaction selector 341 may not change the dialog state until the selectedaction is executed. This may allow the assistant system 140 to executethe dialog state tracker 337 and the action selector 341 for processingspeculative ASR results and to do n-best ranking with dry runs. Inparticular embodiments, the action selector 341 may take the dialogstate update operators as part of the input to select the dialog action.The execution of the dialog action may generate a set of expectation toinstruct the dialog state tracker 337 to handler future turns. Inparticular embodiments, an expectation may be used to provide context tothe dialog state tracker 337 when handling the user input from nextturn. As an example and not by way of limitation, slot request dialogaction may have the expectation of proving a value for the requestedslot.

In particular embodiments, the dialog manager 335 may support multi-turncompositional resolution of slot mentions. For a compositional parsefrom the NLU 210, the resolver may recursively resolve the nested slots.The dialog manager 335 may additionally support disambiguation for thenested slots. As an example and not by way of limitation, the userrequest may be “remind me to call Alex”. The resolver may need to knowwhich Alex to call before creating an actionable reminder to-do entity.The resolver may halt the resolution and set the resolution state whenfurther user clarification is necessary for a particular slot. Thegeneral policy 346 may examine the resolution state and createcorresponding dialog action for user clarification. In dialog statetracker 337, based on the user request and the last dialog action, thedialog manager may update the nested slot. This capability may allow theassistant system 140 to interact with the user not only to collectmissing slot values but also to reduce ambiguity of morecomplex/ambiguous utterances to complete the task. In particularembodiments, the dialog manager may further support requesting missingslots in a nested intent and multi-intent user requests (e.g., “takethis photo and send it to Dad”). In particular embodiments, the dialogmanager 335 may support machine-learning models for more robust dialogexperience. As an example and not by way of limitation, the dialog statetracker 337 may use neural network based models (or any other suitablemachine-learning models) to model belief over task hypotheses. Asanother example and not by way of limitation, for action selector 341,highest priority policy units may comprise white-list/black-listoverrides, which may have to occur by design; middle priority units maycomprise machine-learning models designed for action selection; andlower priority units may comprise rule-based fallbacks when themachine-learning models elect not to handle a situation. In particularembodiments, machine-learning model based general policy unit may helpthe assistant system 140 reduce redundant disambiguation or confirmationsteps, thereby reducing the number of turns to execute the user request.

In particular embodiments, the action execution module 226 may calldifferent agents 350 for task execution. An agent 350 may select amongregistered content providers to complete the action. The data structuremay be constructed by the dialog manager 335 based on an intent and oneor more slots associated with the intent. A dialog policy 345 mayfurther comprise multiple goals related to each other through logicaloperators. In particular embodiments, a goal may be an outcome of aportion of the dialog policy and it may be constructed by the dialogmanager 335. A goal may be represented by an identifier (e.g., string)with one or more named arguments, which parameterize the goal. As anexample and not by way of limitation, a goal with its associated goalargument may be represented as {confirm_artist, args: {artist:“Madonna”}}. In particular embodiments, a dialog policy may be based ona tree-structured representation, in which goals are mapped to leaves ofthe tree. In particular embodiments, the dialog manager 335 may executea dialog policy 345 to determine the next action to carry out. Thedialog policies 345 may comprise generic policy 346 and domain specificpolicies 347, both of which may guide how to select the next systemaction based on the dialog state. In particular embodiments, the taskcompletion component 340 of the action execution module 226 maycommunicate with dialog policies 345 comprised in the dialog arbitrator216 to obtain the guidance of the next system action. In particularembodiments, the action selection component 341 may therefore select anaction based on the dialog intent, the associated content objects, andthe guidance from dialog policies 345.

In particular embodiments, the output of the action execution module 226may be sent to the remote response execution module 232. Specifically,the output of the task completion component 340 of the action executionmodule 226 may be sent to the CU composer 355 of the response executionmodule 226. In alternative embodiments, the selected action may requireone or more agents 350 to be involved. As a result, the task completionmodule 340 may inform the agents 350 about the selected action.Meanwhile, the dialog manager 335 may receive an instruction to updatethe dialog state. As an example and not by way of limitation, the updatemay comprise awaiting agents' 350 response. In particular embodiments,the CU composer 355 may generate a communication content for the userusing a natural-language generator (NLG) 356 based on the output of thetask completion module 340. In particular embodiments, the NLG 356 mayuse different language models and/or language templates to generatenatural language outputs. The generation of natural language outputs maybe application specific. The generation of natural language outputs maybe also personalized for each user. The CU composer 355 may alsodetermine a modality of the generated communication content using the UIpayload generator 357. Since the generated communication content may beconsidered as a response to the user request, the CU composer 355 mayadditionally rank the generated communication content using a responseranker 358. As an example and not by way of limitation, the ranking mayindicate the priority of the response.

In particular embodiments, the response execution module 232 may performdifferent tasks based on the output of the CU composer 355. These tasksmay include writing (i.e., storing/updating) the dialog state 361retrieved from data store 212 and generating responses 362. Inparticular embodiments, the output of CU composer 355 may comprise oneor more of natural-language strings, speech, actions with parameters, orrendered images or videos that can be displayed in a VR headset or ARsmart glass. As a result, the response execution module 232 maydetermine what tasks to perform based on the output of CU composer 355.In particular embodiments, the generated response and the communicationcontent may be sent to the local render output module 242 by theresponse execution module 232. In alternative embodiments, the output ofthe CU composer 355 may be additionally sent to the remote TTS module238 if the determined modality of the communication content is audio.The speech generated by the TTS module 238 and the response generated bythe response execution module 232 may be then sent to the render outputmodule 242.

FIG. 4 illustrates an example diagram flow of processing a user input bythe assistant system 140. As an example and not by way of limitation,the user input may be based on audio signals. In particular embodiments,a mic array 402 of the client system 130 may receive the audio signals(e.g., speech). The audio signals may be transmitted to a process loop404 in a format of audio frames. In particular embodiments, the processloop 404 may send the audio frames for voice activity detection (VAD)406 and wake-on-voice (WoV) detection 408. The detection results may bereturned to the process loop 404. If the WoV detection 408 indicates theuser wants to invoke the assistant system 140, the audio frames togetherwith the VAD 406 result may be sent to an encode unit 410 to generateencoded audio data. After encoding, the encoded audio data may be sentto an encrypt unit 412 for privacy and security purpose, followed by alink unit 414 and decrypt unit 416. After decryption, the audio data maybe sent to a mic driver 418, which may further transmit the audio datato an audio service module 420. In alternative embodiments, the userinput may be received at a wireless device (e.g., Bluetooth device)paired with the client system 130. Correspondingly, the audio data maybe sent from a wireless-device driver 422 (e.g., Bluetooth driver) tothe audio service module 420. In particular embodiments, the audioservice module 420 may determine that the user input can be fulfilled byan application executing on the client system 130. Accordingly, theaudio service module 420 may send the user input to a real-timecommunication (RTC) module 424. The RTC module 424 may deliver audiopackets to a video or audio communication system (e.g., VOIP or videocall). The RTC module 424 may call a relevant application (App) 426 toexecute tasks related to the user input.

In particular embodiments, the audio service module 420 may determinethat the user is requesting assistance that needs the assistant system140 to respond. Accordingly, the audio service module 420 may inform theclient-assistant service module 426. In particular embodiments, theclient-assistant service module 426 may communicate with the assistantorchestrator 206. The assistant orchestrator 206 may determine whetherto use client-side process or server-side process to respond to the userinput. In particular embodiments, the assistant orchestrator 206 maydetermine to use client-side process and inform the client-assistantservice module 426 about such decision. As a result, theclient-assistant service module 426 may call relevant modules to respondto the user input.

In particular embodiments, the client-assistant service module 426 mayuse the local ASR module 216 to analyze the user input. The ASR module216 may comprise a grapheme-to-phoneme (G2P) model, a pronunciationlearning model, a personalized language model (PLM), an end-pointingmodel, and a personalized acoustic model. In particular embodiments, theclient-assistant service module 426 may further use the local NLU module218 to understand the user input. The NLU module 218 may comprise anamed entity resolution (NER) component and a contextual session-basedNLU component. In particular embodiments, the client-assistant servicemodule 426 may use an intent broker 428 to analyze the user's intent. Tobe accurate about the user's intent, the intent broker 428 may access anentity store 430 comprising entities associated with the user and theworld. In alternative embodiments, the user input may be submitted viaan application 432 executing on the client system 130. In this case, aninput manager 434 may receive the user input and analyze it by anapplication environment (App Env) module 436. The analysis result may besent to the application 432 which may further send the analysis resultto the ASR module 216 and NLU module 218. In alternative embodiments,the user input may be directly submitted to the client-assistant servicemodule 426 via an assistant application 438 executing on the clientsystem 130. Then the client-assistant service module 426 may performsimilar procedures based on modules as aforementioned, i.e., the ASRmodule 216, the NLU module 218, and the intent broker 428.

In particular embodiments, the assistant orchestrator 206 may determineto user server-side process. Accordingly, the assistant orchestrator 206may send the user input to one or more computing systems that hostdifferent modules of the assistant system 140. In particularembodiments, a server-assistant service module 301 may receive the userinput from the assistant orchestrator 206. The server-assistant servicemodule 301 may instruct the remote ASR module 208 to analyze the audiodata of the user input. The ASR module 208 may comprise agrapheme-to-phoneme (G2P) model, a pronunciation learning model, apersonalized language model (PLM), an end-pointing model, and apersonalized acoustic model. In particular embodiments, theserver-assistant service module 301 may further instruct the remote NLUmodule 210 to understand the user input. In particular embodiments, theserver-assistant service module 301 may call the remote reasoning model214 to process the output from the ASR module 208 and the NLU module210. In particular embodiments, the reasoning model 214 may performentity resolution and dialog optimization. In particular embodiments,the output of the reasoning model 314 may be sent to the agent 350 forexecuting one or more relevant tasks.

In particular embodiments, the agent 350 may access an ontology module440 to accurately understand the result from entity resolution anddialog optimization so that it can execute relevant tasks accurately.The ontology module 440 may provide ontology data associated with aplurality of predefined domains, intents, and slots. The ontology datamay also comprise the structural relationship between different slotsand domains. The ontology data may further comprise information of howthe slots may be grouped, related within a hierarchy where the higherlevel comprises the domain, and subdivided according to similarities anddifferences. The ontology data may also comprise information of how theslots may be grouped, related within a hierarchy where the higher levelcomprises the topic, and subdivided according to similarities anddifferences. Once the tasks are executed, the agent 350 may return theexecution results together with a task completion indication to thereasoning module 214.

The embodiments disclosed herein may include or be implemented inconjunction with an artificial reality system. Artificial reality is aform of reality that has been adjusted in some manner beforepresentation to a user, which may include, e.g., a virtual reality (VR),an augmented reality (AR), a mixed reality (MR), a hybrid reality, orsome combination and/or derivatives thereof. Artificial reality contentmay include completely generated content or generated content combinedwith captured content (e.g., real-world photographs). The artificialreality content may include video, audio, haptic feedback, or somecombination thereof, and any of which may be presented in a singlechannel or in multiple channels (such as stereo video that produces athree-dimensional effect to the viewer). Additionally, in someembodiments, artificial reality may be associated with applications,products, accessories, services, or some combination thereof, that are,e.g., used to create content in an artificial reality and/or used in(e.g., perform activities in) an artificial reality. The artificialreality system that provides the artificial reality content may beimplemented on various platforms, including a head-mounted display (HMD)connected to a host computer system, a standalone HMD, a mobile deviceor computing system, or any other hardware platform capable of providingartificial reality content to one or more viewers.

On-Device Convolutional Neural Network Models for Assistant Systems

In particular embodiment, the assistant system 140 may implement anatural-language understanding module 218 associated with theclient-side process using a convolutional neural network (CNN) model.The CNN model may be compact and can be efficiently executed on a clientsystem 130, e.g., a smartphone or an AR/VR headset. In particularembodiments, the CNN model may be based on an architecture as disclosedherein. For this architecture, the input may be each single word of anutterance by a user. A word-embedding may be then generated for eachsingle word based on character embeddings corresponding to theindividual characters the word contains. In particular embodiments, theCNN model may calculate probabilities for intents and slots associatedwith the user utterance based on such input. In particular embodiments,the CNN model may achieve competitive performance with significantefficiency and compactness (100×) compared to conventional methods suchas long-short term memory (LSTM) based models. Although this disclosuredescribes particular natural-language understanding models by particularsystems in a particular manner, this disclosure contemplates anysuitable natural-language understanding model by any suitable system inany suitable manner.

In particular embodiments, the assistant system 140 executing locally ona client system 130 may receive, at the client system 130, a user inputcomprising one or more words. Each word may comprise one or morecharacters. The assistant system 140 may then input the one or morewords to a convolutional neural network (CNN) model stored on the clientsystem 130. In particular embodiments, the assistant system 140 mayaccess, from a data store of the client system 130, a plurality ofcharacter-embeddings for a plurality of characters, respectively. Theassistant system 140 may then generate, based on the accessedcharacter-embeddings, one or more word-embeddings for the one or morewords, respectively, by processing the accessed character-embeddingswith one or more convolutional layers and one or more gated linear unitsof the CNN model. In particular embodiments, the assistant system 140may determine, based on an analysis of the one or more word-embeddingsby the CNN model, one or more tasks corresponding to the user input forexecution. The assistant system 140 may further provide, at the clientsystem 130, an output responsive to the user input based on theexecution of the one or more tasks.

FIG. 5 illustrates an example architecture 500 of the CNN model. Inparticular embodiments, the CNN model may be implemented in the NLUmodule 218 stored on the client system 130. The client system 130 may bea mobile client system, which has limited computing power and storagecompared to a central computing system (e.g., mainframe computingsystems, dedicated servers, cloud servers). As an example and not by wayof limitation, the client system 130 may be a personal digital assistant(PDA), a cellular telephone, a smartphone, a smart speaker, a virtualreality (VR) headset, or an augment reality (AR) smart glasses. Inparticular embodiments, the CNN model may comprise a plurality oflayers. The plurality of layers may comprise at least a convolutionallayer 502, a pooling layer 504, a gated linear unit 506, a linear layer508, and a residual connection with gradient clipping. A gated linearunit 506 is a simplified gating mechanism for non-deterministic gatesthat reduce the vanishing gradient problem by having linear unitscoupled to the gates. In FIG. 5, the CNN model is used to understand theuser input 510 “set alarm for 5 pm.” In particular embodiments, theassistant system 140 executing on a client system 130 may parse, by anatural-language understanding module 218 stored on the client system130, the user input 510 into the one or more words. The assistant system140 may then access character-embeddings of individual characters (e.g.,256 embeddings) stored on the client system 130. For each word, theassistant system 140 may generate a word-embedding 512 based on thecharacter-embeddings of the characters it contains (e.g., theword-embedding 512 for “set” may be generated from thecharacter-embeddings for “s”+“e”+“t”). Storing character-embeddingscorresponding to different characters that may form different words andaccessing them when executing the CNN model may be an effective solutionfor addressing the technical challenge of saving storage on the clientsystem 130 when executing the CNN model since the character-embeddingstake much less storage than word-embeddings of all possible words. Inparticular embodiments, generating the one or more word-embeddings 512may be further based on a plurality of dictionary features. Dictionaryfeatures may be considered as tags assigned to each word. As an exampleand not by way of limitation, Seattle may have a dictionary feature“city” and orange may have a dictionary feature “fruit”. Thesedictionary features may give more information about a particular token.As illustrated in FIG. 5, these word-embeddings 512 are fed as inputinto the two main submodules, i.e., an intent-classification submodule520 and a slot-classification submodule 530. Although this disclosuredescribes particular architectures of particular CNN models in aparticular manner, this disclosure contemplates any suitablearchitecture of any suitable CNN model in any suitable manner.

In the intent-classification submodule 520, using convolutions andpooling, the assistant system 140 executing on the client system 130 maygenerate one representation for the user input 510 and thisrepresentation may be projected into the intents space. In particularembodiments, the assistant system 140 may determine one or more intentsassociated with the user input 510 by analyzing the one or moreword-embeddings based on the CNN model. Determining the one or moreintents may comprise the following steps. The assistant system 140 mayfirst generate, by the one or more convolutional layers 502 and one ormore pooling layers 504 of the CNN model, a feature representation forthe user input 510 based on the one or more word-embeddings. Theassistant system 140 may then calculate, by one or more linear layers508 of the CNN model, a plurality of probabilities corresponding to aplurality of intents based on the feature representation. In particularembodiments, the plurality of intents may be considered as candidateintents in the intents space. Each probability may indicate a likelihoodthat a corresponding intent is associated with the user input 510. Theassistant system 140 may further determine, based on the calculatedprobabilities, the one or more intents from the plurality of intents. Asan example and not by way of limitation, the determined intent for “setalarm for 5 pm” may be [IN:SET_ALARM] 522, as indicated in FIG. 5.Although this disclosure describes determining particular intents byparticular systems in a particular manner, this disclosure contemplatesdetermining any suitable intent by any suitable system in any suitablemanner.

In the slot-classification submodule 530, for each word, the assistantsystem 140 executing on the client system 130 may generate arepresentation using the stacked convolution layers 502 and gate linearunits (GLUs) 506. The purpose of using GLUs 506 may be that each wordmay have its own importance and GLUs can capture such importance tounderstand the context of the whole user input 510. As an example andnot by way of limitation, “alarm” may be an important context forunderstanding “5”. This representation is finally projected into theslots space. Using gate linear units (GLUs) 506 may be an effectivesolution for addressing the technical challenge of accuratelyunderstanding a user input 510 because GLUs 506 may be able to capturethe importance of each word in the user input 510 to learn the contextof the user input 510 for accurate understanding. After the GLUs 506, alinear layer 508 may be applied. The linear layer may project aprobability for each word indicating a possible slot associated withthis word. The aforementioned intent projection and slot projection mayuse the same architecture simultaneously, which reduces the modelcomplexity. In particular embodiments, more stacked CNN layers may beneeded as the vocabulary for understanding a user input 510 becomesbigger, which may cause issues such as vanishing and explodinggradients. In particular embodiments, the CNN model may further useresidual connections with gradient clipping to solve these issues. Oncethe intents and slots are determined, they may be provided to subsequentmodules of the assistant system 140 for further processing. Althoughthis disclosure describes using particular gate linear units byparticular systems in a particular manner, this disclosure contemplatesusing any suitable gate linear unit by any suitable system in anysuitable manner.

In particular embodiments, the assistant system 140 executing on theclient system 130 may determine one or more slots associated with theuser input by analyzing the one or more word-embeddings based on the CNNmodel. Determining the one or more slots may comprise the followingsteps. The assistant system 140 may first calculate, by one or morelinear layers 508 of the CNN model, a plurality of probabilitiescorresponding to a plurality of slots based on the one or moreword-embeddings. In particular embodiments, the plurality of slots maybe considered as candidate slots in the slots space. Each probabilitymay indicate a likelihood that a corresponding slot is associated with arespective word. The assistant system 140 may further determine, basedon the calculated probabilities, the one or more slots from theplurality of slots. As an example and not by way of limitation, thedetermined slots for “set alarm for 5 pm” may be [SL:TIME] 532 and[SL:TIME] 534 corresponding to “5” and “pm” respectively, as indicatedin FIG. 5. Although this disclosure describes determining particularslots by particular systems in a particular manner, this disclosurecontemplates determining any suitable slot by any suitable system in anysuitable manner.

In particular embodiments, the assistant system 140 may use one or morespeed-up techniques to make the execution of the CNN model even faster.Such techniques may be used in both offline training and onlineinference of the CNN model. In particular embodiments, during inferencetime, the processing of the accessed character-embeddings with the oneor more convolutional layers 502 of the CNN model may be based on one ormore digital signal processing (DSP) algorithms. The one or more DSPalgorithms may be determined based on hardware components of the clientsystem 130. In addition, the analysis of the one or more word-embeddingsby the CNN model may be also based on the one or more DSP algorithms. Inparticular embodiments, during training time, the plurality of layers ofthe CNN model may be generated based on one or more pruning algorithms.The one or more pruning algorithms may be determined based on hardwarecomponents of the client system 130. As an example and not by way oflimitation, a CNN model running on a remote server may have n (n is anarbitrary number) layers. Based on the limited computing powerdetermined based on the hardware components of a client system 130, theassistant system 140 may determine to prune some of the n layers. As aresult, the CNN-model running on the client system 130 may only have n-m(m is an arbitrary number which is smaller than n) layers. In particularembodiments, the CNN model may comprise a plurality of parameters. Theplurality of parameters may be determined based on one or moresparsification algorithms. To be more specific, sparsificationalgorithms may be used to reduce the number of parameters. As an exampleand not by way of limitation, a CNN model may have p (p is an arbitrarynumber) parameters and sparsification algorithms may help reduce thenumber of parameters from p to k (k is an arbitrary number which issmaller than p). Although this disclosure describes using particularspeed-up techniques by particular systems in a particular manner, thisdisclosure contemplates using any suitable speed-up technique by anysuitable system in any suitable manner.

In particular embodiments, the assistant system 140 executing on theclient system 130 may apply one or more compression techniques to theCNN model to further reduce its size. As an example and not by way oflimitation, a plurality of parameters and a plurality of activationsassociated with the CNN model may be quantized. Applying quantizationmay make the CNN model smaller and also faster to run. Quantizing bothweights and activations may result in a technical advantage thatinference can be carried out using integer only arithmetic which may bemore efficient compared to floating point operations. As an example andnot by way of limitation, original convolutional operators may bereplaced with 8-bit integer convolutional operators. Since both weightsand activations are quantized to an 8-bit quantized integerrepresentation compared to floating point representation, the assistantsystem 140 may get significant savings in both model file size andlatency. In particular embodiments, the quantization may be associatedwith quantization parameters. The quantization parameters may becalculated separately for weights and activations, which may be done bypassing the training data used for training the CNN model from thevalidation set through the original model and calculating necessarystatistics for calculating the quantization parameters. Using digitalsignal processing algorithms, quantization, pruning algorithms, andsparsification algorithms for the CNN model may be effective solutionsfor addressing the technical challenge of making the CNN model compactand efficient as these techniques may effectively reduce the bit-ratethe CNN model needs to handle, the number of layers of the CNN model,and the number of parameters of the CNN model, thereby achieving smallermodel file size and latency. Although this disclosure describes usingparticular compression techniques by particular systems in a particularmanner, this disclosure contemplates using any suitable compressiontechnique by any suitable system in any suitable manner.

In particular embodiments, the CNN model may further comprise one ormore layers based on conditional random fields (CRF). The CRF layers maylearn a set of transition probabilities between different slots, basedon which the CNN model may determine the slot for each word. Inparticular embodiments, the CRF layers may not predict slot for eachword separately. For each word, the CRF layers may calculate theprobability of all possible slots and score all possible sequences for asentence using the learned transition probabilities. The highest scoringsequence may be used to get the slot prediction for each word. Inparticular embodiments, one or more dynamic programming algorithms maybe used for scoring the sentences. As an example and not by way oflimitation, the CRF layers may learn that the chances of occurrence of[SL:TIME] slot and [SL:TARGET_DEST] slot is very low because SL:TIMEoccurs with [IN:SET_ALARM] intent and [SL:TARGET_DEST] occurs with[IN:GET DIRECTION] intent, and usually these do not occur together inthe same sentence. Although this disclosure describes using particularconditional random fields by particular systems in a particular manner,this disclosure contemplates using any suitable conditional random fieldby any suitable system in any suitable manner.

In particular embodiments, the assistant system 140 executing on theclient system 130 may send, to one or more remote servers, the one ormore tasks for execution. Accordingly, the output may be generated bythe one or more remote servers based on the execution of the one or moretasks. In particular embodiments, the assistant system 140 may furtherreceive, at the client system 130 from the one or more remote servers,instructions for providing the output. As can be seen, only the tasksmay be communicated to the remote servers for execution. As a result,the assistant system 140 may have a technical advantage of effectiveprotection of user privacy as the natural-language understanding isperformed on the client system 130, which may guarantee that allsensitive user data is processed locally on the client system 130without being transmitted to a remote server or a third-party system170. Although this disclosure describes particular communication betweenparticular systems in a particular manner, this disclosure contemplatesany suitable communication between any suitable system in any suitablemanner.

The embodiments disclosed herein further disclose a plurality ofexperimental results in comparison with multiple prior baseline methods.In particular embodiments, one experiment is based on user input 510associated with calling and device control domains. Another experimentis based on production domains. Table 1 illustrates experimental resultsbased on calling and device control domains as evaluated by differentmetrics. In Table 1, “Ensemble BiLSTM with CLE+CRF” denotes a firstprior baseline method, which is based on bidirectional long short-termmemory models using contextual language embedding (CLE) plus theconditional random fields. “Single BiLSTM+CRF” denotes a second priorbaseline method, which is based on one bidirectional long short-termmemory model plus the conditional random fields. “DeepCNN+CRF” denotes afirst CNN model based on the embodiments disclosed herein plus theconditional random fields. “DeepCNN (No CRF)” denotes a second CNN modelbased on the embodiments disclosed herein without the conditional randomfields. “Quantized DeepCNN+CRF” denotes a first quantized CNN modelbased on the embodiments disclosed herein plus the conditional randomfields. “Quantized DeepCNN (No CRF)” denotes a second quantized CNNmodel based on the embodiments disclosed herein without the conditionalrandom fields. In particular embodiments, higher frame accuracy (anevaluation metric), lower latency (an evaluation metric for speed), orsmaller model size (an evaluation metric) indicates better performance.“ms” indicates millisecond and “M” indicates megabyte. As indicated byTable 1, the embodiments disclosed herein (i.e., “DeepCNN+CRF”, “DeepCNN(No CRF)”, “Quantized DeepCNN+CRF”, and “Quantized DeepCNN (No CRF)”)have advantage over the two prior baseline methods. As an example andnot by way of limitation, on calling and device control domains (theresults in Table 1), the embodiments disclosed herein are about 150×smaller in size and 190× faster than the baseline production models withabout 2% reduction in model accuracy. Notably, the model of theembodiments disclosed herein i.e., “Quantized DeepCNN (No CRF)”, has amodel size of 1.3 M with a 4 ms latency for sequence length 20. Bycontrast, the first prior baseline method has a model size of 194 M with752 ms latency. As validated by the experiments, the assistant system140 may have a technical advantage of performing natural-languageunderstanding on a local client system 130 with performance comparableto that on a remote server while additionally saving file size andlatency because the CNN model uses an architecture tailored for thelimited computing power and storage of the client system 130.

TABLE 1 Experimental results based on calling and device control domainsas evaluated by different metrics. Frame Latency Latency Latency ModelAccuracy (length 5) (length 10) (length 20) Size Ensemble BiLSTM with93.6% 200 ms 389 ms 752 ms 194 M CLE + CRF Single BiLSTM + CRF 91.6% 20ms 36 ms 79 ms 7 M DeepCNN + CRF 92.2% 10 ms 13 ms 21 ms 7 M DeepCNN (NoCRF) 92.3% 6 ms 9 ms 14 ms 5 M Quantized DeepCNN + 91.2% 3 ms 5 ms 8 ms1.8 M CRF Quantized DeepCNN (No 91.1% 2 ms 3 ms 4 ms 1.3 M CRF)

FIG. 6 illustrates an example method 600 for responding to a user input510 using an on-device CNN model. The method may begin at step 605,where the assistant system 140 executing locally on a client system 130may receive, at the client system 130, a user input 510 comprising oneor more words, wherein each word comprises one or more characters. Atstep 610, the assistant system 140 may parse, by a natural-languageunderstanding module 218 stored on the client system 130, the user input510 into the one or more words. At step 615, the assistant system 140may input the one or more words to a convolutional neural network (CNN)model stored on the client system 130, wherein the CNN model comprises aplurality of layers, wherein the plurality of layers comprise at least aconvolutional layer 502, a pooling layer 504, a gated linear unit 506, alinear layer 508, and a residual connection with gradient clipping,wherein the CNN model is trained based on one or more digital signalprocessing (DSP) algorithms determined based on hardware components ofthe client system 130, and wherein a plurality of parameters and aplurality of activations associated with the CNN model are quantized. Atstep 620, the assistant system 140 may determine one or more intentsassociated with the user input 510 by analyzing the one or moreword-embeddings based on the CNN model. At step 625, the assistantsystem 140 may determine one or more slots associated with the userinput 510 by analyzing the one or more word-embeddings based on the CNNmodel. At step 630, the assistant system 140 may access, from a datastore of the client system 130, a plurality of character-embeddings fora plurality of characters, respectively. At step 635, the assistantsystem 140 may generate, based on the accessed character-embeddings anda plurality of dictionary features, one or more word-embeddings for theone or more words, respectively, by processing the accessedcharacter-embeddings with one or more convolutional layers 502 and oneor more gated linear units 506 of the CNN model. At step 640, theassistant system 140 may determine, based on an analysis of the one ormore word-embeddings by the CNN model, one or more tasks correspondingto the user input 510 for execution. At step 645, the assistant system140 may send, to one or more remote servers, the one or more tasks forexecution, wherein the output is generated by the one or more remoteservers based on the execution of the one or more tasks. At step 650,the assistant system 140 may receive, at the client system 130 from theone or more remote servers, instructions for providing the output. Atstep 655, the assistant system 140 may provide, at the client system130, an output responsive to the user input 510 based on the executionof the one or more tasks. Particular embodiments may repeat one or moresteps of the method of FIG. 6, where appropriate. Although thisdisclosure describes and illustrates particular steps of the method ofFIG. 6 as occurring in a particular order, this disclosure contemplatesany suitable steps of the method of FIG. 6 occurring in any suitableorder. Moreover, although this disclosure describes and illustrates anexample method for responding to a user input using an on-device CNNmodel including the particular steps of the method of FIG. 6, thisdisclosure contemplates any suitable method for responding to a userinput using an on-device CNN model including any suitable steps, whichmay include all, some, or none of the steps of the method of FIG. 6,where appropriate. Furthermore, although this disclosure describes andillustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 6, this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 6.

Social Graphs

FIG. 7 illustrates an example social graph 700. In particularembodiments, the social-networking system 160 may store one or moresocial graphs 700 in one or more data stores. In particular embodiments,the social graph 700 may include multiple nodes—which may includemultiple user nodes 702 or multiple concept nodes 704—and multiple edges706 connecting the nodes. Each node may be associated with a uniqueentity (i.e., user or concept), each of which may have a uniqueidentifier (ID), such as a unique number or username. The example socialgraph 700 illustrated in FIG. 7 is shown, for didactic purposes, in atwo-dimensional visual map representation. In particular embodiments, asocial-networking system 160, a client system 130, an assistant system140, or a third-party system 170 may access the social graph 700 andrelated social-graph information for suitable applications. The nodesand edges of the social graph 700 may be stored as data objects, forexample, in a data store (such as a social-graph database). Such a datastore may include one or more searchable or queryable indexes of nodesor edges of the social graph 700.

In particular embodiments, a user node 702 may correspond to a user ofthe social-networking system 160 or the assistant system 140. As anexample and not by way of limitation, a user may be an individual (humanuser), an entity (e.g., an enterprise, business, or third-partyapplication), or a group (e.g., of individuals or entities) thatinteracts or communicates with or over the social-networking system 160or the assistant system 140. In particular embodiments, when a userregisters for an account with the social-networking system 160, thesocial-networking system 160 may create a user node 702 corresponding tothe user, and store the user node 702 in one or more data stores. Usersand user nodes 702 described herein may, where appropriate, refer toregistered users and user nodes 702 associated with registered users. Inaddition or as an alternative, users and user nodes 702 described hereinmay, where appropriate, refer to users that have not registered with thesocial-networking system 160. In particular embodiments, a user node 702may be associated with information provided by a user or informationgathered by various systems, including the social-networking system 160.As an example and not by way of limitation, a user may provide his orher name, profile picture, contact information, birth date, sex, maritalstatus, family status, employment, education background, preferences,interests, or other demographic information. In particular embodiments,a user node 702 may be associated with one or more data objectscorresponding to information associated with a user. In particularembodiments, a user node 702 may correspond to one or more webinterfaces.

In particular embodiments, a concept node 704 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with the social-networking system 160 or athird-party website associated with a web-application server); an entity(such as, for example, a person, business, group, sports team, orcelebrity); a resource (such as, for example, an audio file, video file,digital photo, text file, structured document, or application) which maybe located within the social-networking system 160 or on an externalserver, such as a web-application server; real or intellectual property(such as, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory;another suitable concept; or two or more such concepts. A concept node704 may be associated with information of a concept provided by a useror information gathered by various systems, including thesocial-networking system 160 and the assistant system 140. As an exampleand not by way of limitation, information of a concept may include aname or a title; one or more images (e.g., an image of the cover page ofa book); a location (e.g., an address or a geographical location); awebsite (which may be associated with a URL); contact information (e.g.,a phone number or an email address); other suitable concept information;or any suitable combination of such information. In particularembodiments, a concept node 704 may be associated with one or more dataobjects corresponding to information associated with concept node 704.In particular embodiments, a concept node 704 may correspond to one ormore web interfaces.

In particular embodiments, a node in the social graph 700 may representor be represented by a web interface (which may be referred to as a“profile interface”). Profile interfaces may be hosted by or accessibleto the social-networking system 160 or the assistant system 140. Profileinterfaces may also be hosted on third-party websites associated with athird-party system 170. As an example and not by way of limitation, aprofile interface corresponding to a particular external web interfacemay be the particular external web interface and the profile interfacemay correspond to a particular concept node 704. Profile interfaces maybe viewable by all or a selected subset of other users. As an exampleand not by way of limitation, a user node 702 may have a correspondinguser-profile interface in which the corresponding user may add content,make declarations, or otherwise express himself or herself. As anotherexample and not by way of limitation, a concept node 704 may have acorresponding concept-profile interface in which one or more users mayadd content, make declarations, or express themselves, particularly inrelation to the concept corresponding to concept node 704.

In particular embodiments, a concept node 704 may represent athird-party web interface or resource hosted by a third-party system170. The third-party web interface or resource may include, among otherelements, content, a selectable or other icon, or other inter-actableobject representing an action or activity. As an example and not by wayof limitation, a third-party web interface may include a selectable iconsuch as “like,” “check-in,” “eat,” “recommend,” or another suitableaction or activity. A user viewing the third-party web interface mayperform an action by selecting one of the icons (e.g., “check-in”),causing a client system 130 to send to the social-networking system 160a message indicating the user's action. In response to the message, thesocial-networking system 160 may create an edge (e.g., a check-in-typeedge) between a user node 702 corresponding to the user and a conceptnode 704 corresponding to the third-party web interface or resource andstore edge 706 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 700 maybe connected to each other by one or more edges 706. An edge 706connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 706 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, the social-networkingsystem 160 may send a “friend request” to the second user. If the seconduser confirms the “friend request,” the social-networking system 160 maycreate an edge 706 connecting the first user's user node 702 to thesecond user's user node 702 in the social graph 700 and store edge 706as social-graph information in one or more of data stores 164. In theexample of FIG. 7, the social graph 700 includes an edge 706 indicatinga friend relation between user nodes 702 of user “A” and user “B” and anedge indicating a friend relation between user nodes 702 of user “C” anduser “B.” Although this disclosure describes or illustrates particularedges 706 with particular attributes connecting particular user nodes702, this disclosure contemplates any suitable edges 706 with anysuitable attributes connecting user nodes 702. As an example and not byway of limitation, an edge 706 may represent a friendship, familyrelationship, business or employment relationship, fan relationship(including, e.g., liking, etc.), follower relationship, visitorrelationship (including, e.g., accessing, viewing, checking-in, sharing,etc.), subscriber relationship, superior/subordinate relationship,reciprocal relationship, non-reciprocal relationship, another suitabletype of relationship, or two or more such relationships. Moreover,although this disclosure generally describes nodes as being connected,this disclosure also describes users or concepts as being connected.Herein, references to users or concepts being connected may, whereappropriate, refer to the nodes corresponding to those users or conceptsbeing connected in the social graph 700 by one or more edges 706. Thedegree of separation between two objects represented by two nodes,respectively, is a count of edges in a shortest path connecting the twonodes in the social graph 700. As an example and not by way oflimitation, in the social graph 700, the user node 702 of user “C” isconnected to the user node 702 of user “A” via multiple paths including,for example, a first path directly passing through the user node 702 ofuser “B,” a second path passing through the concept node 704 of company“Alme” and the user node 702 of user “D,” and a third path passingthrough the user nodes 702 and concept nodes 704 representing school“Stateford,” user “G,” company “Alme,” and user “D.” User “C” and user“A” have a degree of separation of two because the shortest pathconnecting their corresponding nodes (i.e., the first path) includes twoedges 706.

In particular embodiments, an edge 706 between a user node 702 and aconcept node 704 may represent a particular action or activity performedby a user associated with user node 702 toward a concept associated witha concept node 704. As an example and not by way of limitation, asillustrated in FIG. 7, a user may “like,” “attended,” “played,”“listened,” “cooked,” “worked at,” or “read” a concept, each of whichmay correspond to an edge type or subtype. A concept-profile interfacecorresponding to a concept node 704 may include, for example, aselectable “check in” icon (such as, for example, a clickable “check in”icon) or a selectable “add to favorites” icon. Similarly, after a userclicks these icons, the social-networking system 160 may create a“favorite” edge or a “check in” edge in response to a user's actioncorresponding to a respective action. As another example and not by wayof limitation, a user (user “C”) may listen to a particular song(“Imagine”) using a particular application (a third-party online musicapplication). In this case, the social-networking system 160 may createa “listened” edge 706 and a “used” edge (as illustrated in FIG. 7)between user nodes 702 corresponding to the user and concept nodes 704corresponding to the song and application to indicate that the userlistened to the song and used the application. Moreover, thesocial-networking system 160 may create a “played” edge 706 (asillustrated in FIG. 7) between concept nodes 704 corresponding to thesong and the application to indicate that the particular song was playedby the particular application. In this case, “played” edge 706corresponds to an action performed by an external application (thethird-party online music application) on an external audio file (thesong “Imagine”). Although this disclosure describes particular edges 706with particular attributes connecting user nodes 702 and concept nodes704, this disclosure contemplates any suitable edges 706 with anysuitable attributes connecting user nodes 702 and concept nodes 704.Moreover, although this disclosure describes edges between a user node702 and a concept node 704 representing a single relationship, thisdisclosure contemplates edges between a user node 702 and a concept node704 representing one or more relationships. As an example and not by wayof limitation, an edge 706 may represent both that a user likes and hasused at a particular concept. Alternatively, another edge 706 mayrepresent each type of relationship (or multiples of a singlerelationship) between a user node 702 and a concept node 704 (asillustrated in FIG. 7 between user node 702 for user “E” and conceptnode 704 for “online music application”).

In particular embodiments, the social-networking system 160 may createan edge 706 between a user node 702 and a concept node 704 in the socialgraph 700. As an example and not by way of limitation, a user viewing aconcept-profile interface (such as, for example, by using a web browseror a special-purpose application hosted by the user's client system 130)may indicate that he or she likes the concept represented by the conceptnode 704 by clicking or selecting a “Like” icon, which may cause theuser's client system 130 to send to the social-networking system 160 amessage indicating the user's liking of the concept associated with theconcept-profile interface. In response to the message, thesocial-networking system 160 may create an edge 706 between user node702 associated with the user and concept node 704, as illustrated by“like” edge 706 between the user and concept node 704. In particularembodiments, the social-networking system 160 may store an edge 706 inone or more data stores. In particular embodiments, an edge 706 may beautomatically formed by the social-networking system 160 in response toa particular user action. As an example and not by way of limitation, ifa first user uploads a picture, reads a book, watches a movie, orlistens to a song, an edge 706 may be formed between user node 702corresponding to the first user and concept nodes 704 corresponding tothose concepts. Although this disclosure describes forming particularedges 706 in particular manners, this disclosure contemplates formingany suitable edges 706 in any suitable manner.

Vector Spaces and Embeddings

FIG. 8 illustrates an example view of a vector space 800. In particularembodiments, an object or an n-gram may be represented in ad-dimensional vector space, where d denotes any suitable number ofdimensions. Although the vector space 800 is illustrated as athree-dimensional space, this is for illustrative purposes only, as thevector space 800 may be of any suitable dimension. In particularembodiments, an n-gram may be represented in the vector space 800 as avector referred to as a term embedding. Each vector may comprisecoordinates corresponding to a particular point in the vector space 800(i.e., the terminal point of the vector). As an example and not by wayof limitation, vectors 810, 820, and 830 may be represented as points inthe vector space 800, as illustrated in FIG. 8. An n-gram may be mappedto a respective vector representation. As an example and not by way oflimitation, n-grams t₁ and t₂ may be mapped to vectors

and

in the vector space 800, respectively, by applying a function defined bya dictionary, such that

=

(t₁) and

=

(t₂). As another example and not by way of limitation, a dictionarytrained to map text to a vector representation may be utilized, or sucha dictionary may be itself generated via training. As another exampleand not by way of limitation, a word-embeddings model may be used to mapan n-gram to a vector representation in the vector space 800. Inparticular embodiments, an n-gram may be mapped to a vectorrepresentation in the vector space 800 by using a machine leaning model(e.g., a neural network). The machine learning model may have beentrained using a sequence of training data (e.g., a corpus of objectseach comprising n-grams).

In particular embodiments, an object may be represented in the vectorspace 800 as a vector referred to as a feature vector or an objectembedding. As an example and not by way of limitation, objects e₁ and e₂may be mapped to vectors

and

in the vector space 800, respectively, by applying a function

such that

=

(e₁) and

=

(e₂). In particular embodiments, an object may be mapped to a vectorbased on one or more properties, attributes, or features of the object,relationships of the object with other objects, or any other suitableinformation associated with the object. As an example and not by way oflimitation, a function may map objects to vectors by feature extraction,which may start from an initial set of measured data and build derivedvalues (e.g., features). As an example and not by way of limitation, anobject comprising a video or an image may be mapped to a vector by usingan algorithm to detect or isolate various desired portions or shapes ofthe object. Features used to calculate the vector may be based oninformation obtained from edge detection, corner detection, blobdetection, ridge detection, scale-invariant feature transformation, edgedirection, changing intensity, autocorrelation, motion detection,optical flow, thresholding, blob extraction, template matching, Houghtransformation (e.g., lines, circles, ellipses, arbitrary shapes), orany other suitable information. As another example and not by way oflimitation, an object comprising audio data may be mapped to a vectorbased on features such as a spectral slope, a tonality coefficient, anaudio spectrum centroid, an audio spectrum envelope, a Mel-frequencycepstrum, or any other suitable information. In particular embodiments,when an object has data that is either too large to be efficientlyprocessed or comprises redundant data, a function

may map the object to a vector using a transformed reduced set offeatures (e.g., feature selection). In particular embodiments, afunction

may map an object e to a vector

(e) based on one or more n-grams associated with object e. Although thisdisclosure describes representing an n-gram or an object in a vectorspace in a particular manner, this disclosure contemplates representingan n-gram or an object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 160 maycalculate a similarity metric of vectors in vector space 800. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of

and

may be a cosine similarity

$\frac{\overset{arrow}{v_{1}} \cdot \overset{arrow}{v_{2}}}{{\overset{arrow}{v_{1}}}{\overset{arrow}{v_{2}}}}.$

As another example and not by way of limitation, a similarity metric of

and

may be a Euclidean distance ∥

−

∥. A similarity metric of two vectors may represent how similar the twoobjects or n-grams corresponding to the two vectors, respectively, areto one another, as measured by the distance between the two vectors inthe vector space 800. As an example and not by way of limitation, vector810 and vector 820 may correspond to objects that are more similar toone another than the objects corresponding to vector 810 and vector 830,based on the distance between the respective vectors. Although thisdisclosure describes calculating a similarity metric between vectors ina particular manner, this disclosure contemplates calculating asimilarity metric between vectors in any suitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949,436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286,315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365,789, filed 30 Nov. 2016, each of which is incorporated byreference.

Artificial Neural Networks

FIG. 9 illustrates an example artificial neural network (“ANN”) 900. Inparticular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 900 may comprise an inputlayer 910, hidden layers 920, 930, 940, and an output layer 950. Eachlayer of the ANN 900 may comprise one or more nodes, such as a node 905or a node 915. In particular embodiments, each node of an ANN may beconnected to another node of the ANN. As an example and not by way oflimitation, each node of the input layer 910 may be connected to one ofmore nodes of the hidden layer 920. In particular embodiments, one ormore nodes may be a bias node (e.g., a node in a layer that is notconnected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 9 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 9 depicts a connection between each node of the inputlayer 910 and each node of the hidden layer 920, one or more nodes ofthe input layer 910 may not be connected to one or more nodes of thehidden layer 920.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 920 may comprise the output of one or morenodes of the input layer 910. As another example and not by way oflimitation, the input to each node of the output layer 950 may comprisethe output of one or more nodes of the hidden layer 940. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N−1, x may be the input into residual blockN−1. Although this disclosure describes a particular ANN, thisdisclosure contemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}( s_{k} )} = \frac{1}{1 + e^{- s_{k}}}},$

the hyperbolic tangent function

${{F_{k}( s_{k} )} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$

the rectifier F_(k)(s_(k))=max(0, s_(k)), or any other suitable functionF_(k)(s_(k)), where s_(k) may be the effective input to node k. Inparticular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection925 between the node 905 and the node 915 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 905 is used as an input to the node 915. As another exampleand not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k)(s_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j)(w_(jk)x_(j)) may be the effectiveinput to node k, x_(j) may be the output of a node j connected to nodek, and w_(jk) may be the weighting coefficient between node j and nodek. In particular embodiments, the input to nodes of the input layer maybe based on a vector representing an object. Although this disclosuredescribes particular inputs to and outputs of nodes, this disclosurecontemplates any suitable inputs to and outputs of nodes. Moreover,although this disclosure may describe particular connections and weightsbetween nodes, this disclosure contemplates any suitable connections andweights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN 900 and an expected output. As another example and notby way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Privacy

In particular embodiments, one or more objects (e.g., content or othertypes of objects) of a computing system may be associated with one ormore privacy settings. The one or more objects may be stored on orotherwise associated with any suitable computing system or application,such as, for example, a social-networking system 160, a client system130, an assistant system 140, a third-party system 170, asocial-networking application, an assistant application, a messagingapplication, a photo-sharing application, or any other suitablecomputing system or application. Although the examples discussed hereinare in the context of an online social network, these privacy settingsmay be applied to any other suitable computing system. Privacy settings(or “access settings”) for an object may be stored in any suitablemanner, such as, for example, in association with the object, in anindex on an authorization server, in another suitable manner, or anysuitable combination thereof. A privacy setting for an object mayspecify how the object (or particular information associated with theobject) can be accessed, stored, or otherwise used (e.g., viewed,shared, modified, copied, executed, surfaced, or identified) within theonline social network. When privacy settings for an object allow aparticular user or other entity to access that object, the object may bedescribed as being “visible” with respect to that user or other entity.As an example and not by way of limitation, a user of the online socialnetwork may specify privacy settings for a user-profile page thatidentify a set of users that may access work-experience information onthe user-profile page, thus excluding other users from accessing thatinformation.

In particular embodiments, privacy settings for an object may specify a“blocked list” of users or other entities that should not be allowed toaccess certain information associated with the object. In particularembodiments, the blocked list may include third-party entities. Theblocked list may specify one or more users or entities for which anobject is not visible. As an example and not by way of limitation, auser may specify a set of users who may not access photo albumsassociated with the user, thus excluding those users from accessing thephoto albums (while also possibly allowing certain users not within thespecified set of users to access the photo albums). In particularembodiments, privacy settings may be associated with particularsocial-graph elements. Privacy settings of a social-graph element, suchas a node or an edge, may specify how the social-graph element,information associated with the social-graph element, or objectsassociated with the social-graph element can be accessed using theonline social network. As an example and not by way of limitation, aparticular concept node 704 corresponding to a particular photo may havea privacy setting specifying that the photo may be accessed only byusers tagged in the photo and friends of the users tagged in the photo.In particular embodiments, privacy settings may allow users to opt in toor opt out of having their content, information, or actionsstored/logged by the social-networking system 160 or assistant system140 or shared with other systems (e.g., a third-party system 170).Although this disclosure describes using particular privacy settings ina particular manner, this disclosure contemplates using any suitableprivacy settings in any suitable manner.

In particular embodiments, privacy settings may be based on one or morenodes or edges of a social graph 700. A privacy setting may be specifiedfor one or more edges 706 or edge-types of the social graph 700, or withrespect to one or more nodes 702, 704 or node-types of the social graph700. The privacy settings applied to a particular edge 706 connectingtwo nodes may control whether the relationship between the two entitiescorresponding to the nodes is visible to other users of the onlinesocial network. Similarly, the privacy settings applied to a particularnode may control whether the user or concept corresponding to the nodeis visible to other users of the online social network. As an exampleand not by way of limitation, a first user may share an object to thesocial-networking system 160. The object may be associated with aconcept node 704 connected to a user node 702 of the first user by anedge 706. The first user may specify privacy settings that apply to aparticular edge 706 connecting to the concept node 704 of the object, ormay specify privacy settings that apply to all edges 706 connecting tothe concept node 704. As another example and not by way of limitation,the first user may share a set of objects of a particular object-type(e.g., a set of images). The first user may specify privacy settingswith respect to all objects associated with the first user of thatparticular object-type as having a particular privacy setting (e.g.,specifying that all images posted by the first user are visible only tofriends of the first user and/or users tagged in the images).

In particular embodiments, the social-networking system 160 may presenta “privacy wizard” (e.g., within a webpage, a module, one or more dialogboxes, or any other suitable interface) to the first user to assist thefirst user in specifying one or more privacy settings. The privacywizard may display instructions, suitable privacy-related information,current privacy settings, one or more input fields for accepting one ormore inputs from the first user specifying a change or confirmation ofprivacy settings, or any suitable combination thereof. In particularembodiments, the social-networking system 160 may offer a “dashboard”functionality to the first user that may display, to the first user,current privacy settings of the first user. The dashboard functionalitymay be displayed to the first user at any appropriate time (e.g.,following an input from the first user summoning the dashboardfunctionality, following the occurrence of a particular event or triggeraction). The dashboard functionality may allow the first user to modifyone or more of the first user's current privacy settings at any time, inany suitable manner (e.g., redirecting the first user to the privacywizard).

Privacy settings associated with an object may specify any suitablegranularity of permitted access or denial of access. As an example andnot by way of limitation, access or denial of access may be specifiedfor particular users (e.g., only me, my roommates, my boss), userswithin a particular degree-of-separation (e.g., friends,friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 170, particular applications(e.g., third-party applications, external websites), other suitableentities, or any suitable combination thereof. Although this disclosuredescribes particular granularities of permitted access or denial ofaccess, this disclosure contemplates any suitable granularities ofpermitted access or denial of access.

In particular embodiments, one or more servers 162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 164, the social-networking system 160 maysend a request to the data store 164 for the object. The request mayidentify the user associated with the request and the object may be sentonly to the user (or a client system 130 of the user) if theauthorization server determines that the user is authorized to accessthe object based on the privacy settings associated with the object. Ifthe requesting user is not authorized to access the object, theauthorization server may prevent the requested object from beingretrieved from the data store 164 or may prevent the requested objectfrom being sent to the user. In the search-query context, an object maybe provided as a search result only if the querying user is authorizedto access the object, e.g., if the privacy settings for the object allowit to be surfaced to, discovered by, or otherwise visible to thequerying user. In particular embodiments, an object may representcontent that is visible to a user through a newsfeed of the user. As anexample and not by way of limitation, one or more objects may be visibleto a user's “Trending” page. In particular embodiments, an object maycorrespond to a particular user. The object may be content associatedwith the particular user, or may be the particular user's account orinformation stored on the social-networking system 160, or othercomputing system. As an example and not by way of limitation, a firstuser may view one or more second users of an online social networkthrough a “People You May Know” function of the online social network,or by viewing a list of friends of the first user. As an example and notby way of limitation, a first user may specify that they do not wish tosee objects associated with a particular second user in their newsfeedor friends list. If the privacy settings for the object do not allow itto be surfaced to, discovered by, or visible to the user, the object maybe excluded from the search results. Although this disclosure describesenforcing privacy settings in a particular manner, this disclosurecontemplates enforcing privacy settings in any suitable manner.

In particular embodiments, different objects of the same type associatedwith a user may have different privacy settings. Different types ofobjects associated with a user may have different types of privacysettings. As an example and not by way of limitation, a first user mayspecify that the first user's status updates are public, but any imagesshared by the first user are visible only to the first user's friends onthe online social network. As another example and not by way oflimitation, a user may specify different privacy settings for differenttypes of entities, such as individual users, friends-of-friends,followers, user groups, or corporate entities. As another example andnot by way of limitation, a first user may specify a group of users thatmay view videos posted by the first user, while keeping the videos frombeing visible to the first user's employer. In particular embodiments,different privacy settings may be provided for different user groups oruser demographics. As an example and not by way of limitation, a firstuser may specify that other users who attend the same university as thefirst user may view the first user's pictures, but that other users whoare family members of the first user may not view those same pictures.

In particular embodiments, the social-networking system 160 may provideone or more default privacy settings for each object of a particularobject-type. A privacy setting for an object that is set to a defaultmay be changed by a user associated with that object. As an example andnot by way of limitation, all images posted by a first user may have adefault privacy setting of being visible only to friends of the firstuser and, for a particular image, the first user may change the privacysetting for the image to be visible to friends and friends-of-friends.

In particular embodiments, privacy settings may allow a first user tospecify (e.g., by opting out, by not opting in) whether thesocial-networking system 160 or assistant system 140 may receive,collect, log, or store particular objects or information associated withthe user for any purpose. In particular embodiments, privacy settingsmay allow the first user to specify whether particular applications orprocesses may access, store, or use particular objects or informationassociated with the user. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed, stored,or used by specific applications or processes. The social-networkingsystem 160 or assistant system 140 may access such information in orderto provide a particular function or service to the first user, withoutthe social-networking system 160 or assistant system 140 having accessto that information for any other purposes. Before accessing, storing,or using such objects or information, the social-networking system 160or assistant system 140 may prompt the user to provide privacy settingsspecifying which applications or processes, if any, may access, store,or use the object or information prior to allowing any such action. Asan example and not by way of limitation, a first user may transmit amessage to a second user via an application related to the online socialnetwork (e.g., a messaging app), and may specify privacy settings thatsuch messages should not be stored by the social-networking system 160or assistant system 140.

In particular embodiments, a user may specify whether particular typesof objects or information associated with the first user may beaccessed, stored, or used by the social-networking system 160 orassistant system 140. As an example and not by way of limitation, thefirst user may specify that images sent by the first user through thesocial-networking system 160 or assistant system 140 may not be storedby the social-networking system 160 or assistant system 140. As anotherexample and not by way of limitation, a first user may specify thatmessages sent from the first user to a particular second user may not bestored by the social-networking system 160 or assistant system 140. Asyet another example and not by way of limitation, a first user mayspecify that all objects sent via a particular application may be savedby the social-networking system 160 or assistant system 140.

In particular embodiments, privacy settings may allow a first user tospecify whether particular objects or information associated with thefirst user may be accessed from particular client systems 130 orthird-party systems 170. The privacy settings may allow the first userto opt in or opt out of having objects or information accessed from aparticular device (e.g., the phone book on a user's smart phone), from aparticular application (e.g., a messaging app), or from a particularsystem (e.g., an email server). The social-networking system 160 orassistant system 140 may provide default privacy settings with respectto each device, system, or application, and/or the first user may beprompted to specify a particular privacy setting for each context. As anexample and not by way of limitation, the first user may utilize alocation-services feature of the social-networking system 160 orassistant system 140 to provide recommendations for restaurants or otherplaces in proximity to the user. The first user's default privacysettings may specify that the social-networking system 160 or assistantsystem 140 may use location information provided from a client device130 of the first user to provide the location-based services, but thatthe social-networking system 160 or assistant system 140 may not storethe location information of the first user or provide it to anythird-party system 170. The first user may then update the privacysettings to allow location information to be used by a third-partyimage-sharing application in order to geo-tag photos.

In particular embodiments, privacy settings may allow a user to specifyone or more geographic locations from which objects can be accessed.Access or denial of access to the objects may depend on the geographiclocation of a user who is attempting to access the objects. As anexample and not by way of limitation, a user may share an object andspecify that only users in the same city may access or view the object.As another example and not by way of limitation, a first user may sharean object and specify that the object is visible to second users onlywhile the first user is in a particular location. If the first userleaves the particular location, the object may no longer be visible tothe second users. As another example and not by way of limitation, afirst user may specify that an object is visible only to second userswithin a threshold distance from the first user. If the first usersubsequently changes location, the original second users with access tothe object may lose access, while a new group of second users may gainaccess as they come within the threshold distance of the first user.

In particular embodiments, the social-networking system 160 or assistantsystem 140 may have functionalities that may use, as inputs, personal orbiometric information of a user for user-authentication orexperience-personalization purposes. A user may opt to make use of thesefunctionalities to enhance their experience on the online socialnetwork. As an example and not by way of limitation, a user may providepersonal or biometric information to the social-networking system 160 orassistant system 140. The user's privacy settings may specify that suchinformation may be used only for particular processes, such asauthentication, and further specify that such information may not beshared with any third-party system 170 or used for other processes orapplications associated with the social-networking system 160 orassistant system 140. As another example and not by way of limitation,the social-networking system 160 may provide a functionality for a userto provide voice-print recordings to the online social network. As anexample and not by way of limitation, if a user wishes to utilize thisfunction of the online social network, the user may provide a voicerecording of his or her own voice to provide a status update on theonline social network. The recording of the voice-input may be comparedto a voice print of the user to determine what words were spoken by theuser. The user's privacy setting may specify that such voice recordingmay be used only for voice-input purposes (e.g., to authenticate theuser, to send voice messages, to improve voice recognition in order touse voice-operated features of the online social network), and furtherspecify that such voice recording may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160. As another example and not by way oflimitation, the social-networking system 160 may provide a functionalityfor a user to provide a reference image (e.g., a facial profile, aretinal scan) to the online social network. The online social networkmay compare the reference image against a later-received image input(e.g., to authenticate the user, to tag the user in photos). The user'sprivacy setting may specify that such image may be used only for alimited purpose (e.g., authentication, tagging the user in photos), andfurther specify that such image may not be shared with any third-partysystem 170 or used by other processes or applications associated withthe social-networking system 160.

Systems and Methods

FIG. 10 illustrates an example computer system 1000. In particularembodiments, one or more computer systems 1000 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1000 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1000 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1000.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1000. This disclosure contemplates computer system 1000 taking anysuitable physical form. As example and not by way of limitation,computer system 1000 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, or a combination of two or more of these. Whereappropriate, computer system 1000 may include one or more computersystems 1000; be unitary or distributed; span multiple locations; spanmultiple machines; span multiple data centers; or reside in a cloud,which may include one or more cloud components in one or more networks.Where appropriate, one or more computer systems 1000 may perform withoutsubstantial spatial or temporal limitation one or more steps of one ormore methods described or illustrated herein. As an example and not byway of limitation, one or more computer systems 1000 may perform in realtime or in batch mode one or more steps of one or more methods describedor illustrated herein. One or more computer systems 1000 may perform atdifferent times or at different locations one or more steps of one ormore methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 1000 includes a processor1002, memory 1004, storage 1006, an input/output (I/O) interface 1008, acommunication interface 1010, and a bus 1012. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1002 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1002 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1004, or storage 1006; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1004, or storage 1006. In particularembodiments, processor 1002 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1002 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1002 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1004 or storage 1006, and the instruction caches may speed upretrieval of those instructions by processor 1002. Data in the datacaches may be copies of data in memory 1004 or storage 1006 forinstructions executing at processor 1002 to operate on; the results ofprevious instructions executed at processor 1002 for access bysubsequent instructions executing at processor 1002 or for writing tomemory 1004 or storage 1006; or other suitable data. The data caches mayspeed up read or write operations by processor 1002. The TLBs may speedup virtual-address translation for processor 1002. In particularembodiments, processor 1002 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1002 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1002 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1002. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1004 includes main memory for storinginstructions for processor 1002 to execute or data for processor 1002 tooperate on. As an example and not by way of limitation, computer system1000 may load instructions from storage 1006 or another source (such as,for example, another computer system 1000) to memory 1004. Processor1002 may then load the instructions from memory 1004 to an internalregister or internal cache. To execute the instructions, processor 1002may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1002 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1002 may then write one or more of those results to memory 1004. Inparticular embodiments, processor 1002 executes only instructions in oneor more internal registers or internal caches or in memory 1004 (asopposed to storage 1006 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1004 (asopposed to storage 1006 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1002 to memory 1004. Bus 1012 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1002 and memory 1004and facilitate accesses to memory 1004 requested by processor 1002. Inparticular embodiments, memory 1004 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1004 may include one ormore memories 1004, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1006 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1006 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1006 may include removable or non-removable (or fixed)media, where appropriate. Storage 1006 may be internal or external tocomputer system 1000, where appropriate. In particular embodiments,storage 1006 is non-volatile, solid-state memory. In particularembodiments, storage 1006 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1006taking any suitable physical form. Storage 1006 may include one or morestorage control units facilitating communication between processor 1002and storage 1006, where appropriate. Where appropriate, storage 1006 mayinclude one or more storages 1006. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1008 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1000 and one or more I/O devices. Computersystem 1000 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1000. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1008 for them. Where appropriate, I/Ointerface 1008 may include one or more device or software driversenabling processor 1002 to drive one or more of these I/O devices. I/Ointerface 1008 may include one or more I/O interfaces 1008, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1010 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1000 and one or more other computer systems 1000 or oneor more networks. As an example and not by way of limitation,communication interface 1010 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1010 for it. As an example and not by way oflimitation, computer system 1000 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1000 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1000 may include any suitable communicationinterface 1010 for any of these networks, where appropriate.Communication interface 1010 may include one or more communicationinterfaces 1010, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1012 includes hardware, software, or bothcoupling components of computer system 1000 to each other. As an exampleand not by way of limitation, bus 1012 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1012may include one or more buses 1012, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

MISCELLANEOUS

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by a client system:receiving, at the client system, a user input comprising one or morewords, wherein each word comprises one or more characters; inputting theone or more words to a convolutional neural network (CNN) model storedon the client system; accessing, from a data store of the client system,a plurality of character-embeddings for a plurality of characters,respectively; generating, based on the accessed character-embeddings,one or more word-embeddings for the one or more words, respectively, byprocessing the accessed character-embeddings with one or moreconvolutional layers and one or more gated linear units of the CNNmodel; determining, based on an analysis of the one or moreword-embeddings by the CNN model, one or more tasks corresponding to theuser input for execution; and providing, at the client system, an outputresponsive to the user input based on the execution of the one or moretasks.
 2. The method of claim 1, further comprising: parsing, by anatural-language understanding module stored on the client system, theuser input into the one or more words.
 3. The method of claim 1, whereinthe CNN model comprises a plurality of layers, wherein the plurality oflayers comprise at least a convolutional layer, a pooling layer, a gatedlinear unit, a linear layer, and a residual connection with gradientclipping.
 4. The method of claim 1, further comprising: determining oneor more intents associated with the user input by analyzing the one ormore word-embeddings based on the CNN model.
 5. The method of claim 4,wherein determining the one or more intents comprises: generating, bythe one or more convolutional layers and one or more pooling layers ofthe CNN model, a feature representation for the user input based on theone or more word-embeddings; calculating, by one or more linear layersof the CNN model, a plurality of probabilities corresponding to aplurality of intents based on the feature representation, wherein eachprobability indicates a likelihood that a corresponding intent isassociated with the user input; and determining, based on the calculatedprobabilities, the one or more intents from the plurality of intents. 6.The method of claim 1, further comprising: determining one or more slotsassociated with the user input by analyzing the one or moreword-embeddings based on the CNN model.
 7. The method of claim 6,wherein determining the one or more slots comprises: calculating, by oneor more linear layers of the CNN model, a plurality of probabilitiescorresponding to a plurality of slots based on the one or moreword-embeddings, wherein each probability indicates a likelihood that acorresponding slot is associated with a respective word; anddetermining, based on the calculated probabilities, the one or moreslots from the plurality of slots.
 8. The method of claim 1, wherein theprocessing of the accessed character-embeddings with the one or moreconvolutional layers of the CNN model is based on one or more digitalsignal processing (DSP) algorithms, wherein the one or more DSPalgorithms are determined based on hardware components of the clientsystem.
 9. The method of claim 1, wherein the analysis of the one ormore word-embeddings by the CNN model is based on one or more digitalsignal processing (DSP) algorithms, wherein the one or more DSPalgorithms are determined based on hardware components of the clientsystem.
 10. The method of claim 1, wherein generating the one or moreword-embeddings is further based on a plurality of dictionary features.11. The method of claim 1, wherein a plurality of parameters and aplurality of activations associated with the CNN model are quantized.12. The method of claim 1, further comprising: sending, to one or moreremote servers, the one or more tasks for execution, wherein the outputis generated by the one or more remote servers based on the execution ofthe one or more tasks.
 13. The method of claim 12, further comprising:receiving, at the client system from the one or more remote servers,instructions for providing the output.
 14. The method of claim 1,wherein the CNN model comprises a plurality of layers, wherein theplurality of layers are generated based on one or more pruningalgorithms, wherein the one or more pruning algorithms are determinedbased on hardware components of the client system.
 15. The method ofclaim 1, wherein the CNN model comprises a plurality of parameters,wherein the plurality of parameters are determined based on one or moresparsification algorithms.
 16. One or more computer-readablenon-transitory storage media embodying software that is operable whenexecuted to: receive, at the client system, a user input comprising oneor more words, wherein each word comprises one or more characters; inputthe one or more words to a convolutional neural network (CNN) modelstored on the client system; access, from a data store of the clientsystem, a plurality of character-embeddings for a plurality ofcharacters, respectively; generate, based on the accessedcharacter-embeddings, one or more word-embeddings for the one or morewords, respectively, by processing the accessed character-embeddingswith one or more convolutional layers and one or more gated linear unitsof the CNN model; determine, based on an analysis of the one or moreword-embeddings by the CNN model, one or more tasks corresponding to theuser input for execution; and provide, at the client system, an outputresponsive to the user input based on the execution of the one or moretasks.
 17. The media of claim 16, wherein the software is furtheroperable when executed to: parse, by a natural-language understandingmodule stored on the client system, the user input into the one or morewords.
 18. The media of claim 16, wherein the CNN model comprises aplurality of layers, wherein the plurality of layers comprise at least aconvolutional layer, a pooling layer, a gated linear unit, a linearlayer, and a residual connection with gradient clipping.
 19. The mediaof claim 16, wherein the software is further operable when executed to:determine one or more intents associated with the user input byanalyzing the one or more word-embeddings based on the CNN model.
 20. Asystem comprising: one or more processors; and a non-transitory memorycoupled to the processors comprising instructions executable by theprocessors, the processors operable when executing the instructions to:receive, at the client system, a user input comprising one or morewords, wherein each word comprises one or more characters; input the oneor more words to a convolutional neural network (CNN) model stored onthe client system; access, from a data store of the client system, aplurality of character-embeddings for a plurality of characters,respectively; generate, based on the accessed character-embeddings, oneor more word-embeddings for the one or more words, respectively, byprocessing the accessed character-embeddings with one or moreconvolutional layers and one or more gated linear units of the CNNmodel; determine, based on an analysis of the one or moreword-embeddings by the CNN model, one or more tasks corresponding to theuser input for execution; and provide, at the client system, an outputresponsive to the user input based on the execution of the one or moretasks.